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Journal articles on the topic 'Recognition of characters'
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Omar, Bayan. "Individuality Representation in Character Recognition." Journal of University of Human Development 1, no. 2 (April 30, 2015): 300. http://dx.doi.org/10.21928/juhd.v1n2y2015.pp300-305.
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The task of recognition that is based on handwriting characters in the Kurdish language is an interesting study in the area of computer vision and pattern recognition. In the past couple of years, numerous state-of-the-art techniques and methods have been created for pattern recognition. On the other hand, Kurdish language handwriting recognition has been seen to be more difficult when compared to other different languages. The similarities in the properties in Kurdish characters is the primary reason of the great resemblance in the features of Kurdish handwriting characters, therefore the requirement for the recognition process is critical. Consequently, to obtain accurate and precise recognition on the basis of the Kurdish handwriting character, it is crucial for the resemblances in the character properties of Kurdish handwriting to be distinguished. To identify a particular character, the style of character handwriting may be evaluated to enable the implied representation of the hidden unique features of the user’s character. Unique features may guide in recognizing characters that may be important when recognizing the correct character among similar characters. On the other hand, the problem of the resemblances in the properties of handwriting of Kurdish characters were not taken into account ,consequently leaving a high chance of reducing the similarity error for any intra-class (of the same character),with the reduction of the similarity error for any inter-class (of different characters) as well. In order to obtain higher effectiveness, this study uses discretization features for reducing the similarity error for intra-class (of the same character),with the increase of the similarity error for inter-class (of different characters)in recognition of Kurdish Handwriting characters with MAE.
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Devaraj, Anjali Yogesh, Anup S. Jain, Omisha N, and Shobana TS. "Kannada Text Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 73–78. http://dx.doi.org/10.22214/ijraset.2022.46520.
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Abstract: The task of automatic handwriting recognition is critical. This can be a difficult subject, and it has gotten a lot of attention in recent years. In the realm of picture grouping, handwritten character recognition is a problem. Handwritten characters are difficult to decipher since various people have distinct handwriting styles. For decades, researchers have been focusing on character identification in Latin handwriting. Kannada has had fewer studies conducted on it. Our "Kannada Text Recognition" research and effort attempts to classify and recognize characters written in Kannada, a south Indian language. The characters are taken from written documents, preprocessed with numpy and OpenCV, and then run through a CNN.
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Huynh, Loc Huu, Hai Quoc Luu, and Vu Duc Anh Dinh. "MODIFIED DIRECTION FEATURE AND NEURAL NETWORK BASED TECHNIQUE FOR HANDWRITING CHARACTER RECOGNITION." Science and Technology Development Journal 14, no. 2 (June 30, 2011): 62–70. http://dx.doi.org/10.32508/stdj.v14i2.1910.
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Handwriting character recognition is an important research topic which has various applications in surveillance, radar, robot technology... In this paper, we propose the implementation of the handwriting character recognition using off-line handwriting recognition. The approach consists of two steps: to make thin handwriting by keeping the skeleton of character and reject redundant points caused by humam’s stroke width and to modify direction method which provide high accuracy and simply structure analysis method to extract character’s features from its skeleton. In addition, we build neural network in order to help machine learn character specific features and create knowledge databases to help them have ability to classify character with other characters. The recognition accuracy of above 84% is reported on characters from real samples. Using this off-line system and other parts in handwriting text recognition, we can replace or cooperate with online recognition techniques which are ususally applied on mobile devices and extend our handwriting recognition technique on any surfaces such as papers, boards, and vehicle lisences as well as provide the reading ability for humanoid robot.
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Khan, Sulaiman, and Shah Nazir. "Deep Learning Based Pashto Characters Recognition." Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences 58, no. 3 (February 3, 2022): 49–58. http://dx.doi.org/10.53560/ppasa(58-3)743.
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In artificial intelligence, text identification and analysis that are based on images play a vital role in the text retrieving process. Automatic text recognition system development is a difficult task in machine learning, but in the case of cursive languages, it poses a big challenge to the research community due to slight changes in character’s shapes and the unavailability of a standard dataset. While this recognition task becomes more challenging in the case of Pashto language due to a large number of characters in its dataset than other similar cursive languages (Persian, Urdu, Arabic) and a slight change in character’s shape. This paper aims to address accept these challenges by developing an optimal optical character recognition (OCR) system to recognise isolated handwritten Pashto characters. The proposed OCR system is developed using multiple long short-term memory (LSTM) based deep learning model. The applicability of the proposed model is validated by using the decision trees (DT) classification tool based on the zoning feature extraction technique and the invariant moment approaches. An overall accuracy rate of 89.03% is calculated for the multiple LSTM-based OCR system while DT-based recognition rate of 72.9% is achieved using zoning feature vector and 74.56% is achieved for invariant moments-based feature map. Applicability of the system is evaluated using different performance metrics of accuracy, f-score, specificity, and varying training and test sets.
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Siddiqui, Sayma Shafeeque A. W., Rajashri G. Kanke, Ramnath M. Gaikwad, and Manasi R. Baheti. "Review on Isolated Urdu Character Recognition: Offline Handwritten Approach." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (August 31, 2023): 384–88. http://dx.doi.org/10.22214/ijraset.2023.55164.
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Abstract: This paper summarizes a system for recognizing isolated Urdu characters using advanced machine learning algorithms. The system analyzes visual features of Urdu characters, like strokes and curves, to train models such as CNN, SVM, ANN, and MLP. With a large dataset, the system can accurately predict unseen characters. It can be integrated into various applications for real-time character recognition tasks like OCR (Optical Character Recognition) and handwriting recognition. This literature survey explores research papers focused on character recognition in languages like Urdu, Arabic, Persian, and Sindhi, proposing various techniques like feature extraction, deep learning, and machine learning to enhance character recognition technology. The survey highlights specific studies with high accuracy and discusses recognition systems for Arabic characters as well.
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MALIK, LATESH, and P. S. DESHPANDE. "RECOGNITION OF HANDWRITTEN DEVANAGARI SCRIPT." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 05 (August 2010): 809–22. http://dx.doi.org/10.1142/s0218001410008123.
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Segmentation of handwritten text into lines, words and characters is one of the important steps in the handwritten text recognition process. In this paper, we propose a float fill algorithm for segmentation of unconstrained Devanagari text into words. Here, a text image is directly segmented into individual words. Rectangular boundaries are drawn around the words and horizontal lines are detected with template matching. A mask is designed for detecting the horizontal line and is applied to each word from left to right and top to bottom of the document. Header lines are removed for character separation. A new segment code features are extracted for each character. In this paper, we present the results of multiple classifier combination for offline handwritten Devanagari characters. The use of regular expressions in handwritten characters is a novel concept and they are defined in a manner so that they can become more robust to noise. We have achieved an accuracy of 94% for word level segmentation, 95% for coarse classification and 85% for fine classification of character recognition. On experimentation with a dataset of 5000 samples of characters, the overall recognition rate observed is 95% as we considered top five choice results. The proposed combined classifier can be applied to handwritten character recognition of any other language like English, Chinese, Arabic, etc. and can recognize the characters with same accuracy.18 For printed characters we have achieved accuracy of 100%, only by applying the regular expression classifier.17
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Jbrail, Mohammed Widad, and Mehmet Emin Tenekeci. "Character Recognition of Arabic Handwritten Characters Using Deep Learning." Journal of Studies in Science and Engineering 2, no. 1 (March 19, 2022): 32–40. http://dx.doi.org/10.53898/josse2022213.
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Optical character recognition (OCR) is used to digitize texts in printed documents and camera images. The most basic step in the OCR process is character recognition. The Arabic language is more complex than other alphabets, as the cursive is written in cursive and the characters have different spellings. Our research has improved a character recognition model for Arabic texts with 28 different characters. Character recognition was performed using Convolutional Neural Network models, which are accepted as effective in image processing and recognition. Three different CNN models have been proposed. In the study, training and testing of the models were carried out using the Hijja data set. Among the proposed models, Model C with a 99.3% accuracy rate has obtained results that can compete with the studies in the literature.
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R, Spurgen Ratheash, and Mohamed Sathik M. "Tamil Character Recognition in Palm Leaf Manuscripts." International Research Journal of Tamil 3, no. 2 (April 26, 2021): 70–77. http://dx.doi.org/10.34256/irjt21210.
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Tamil characters are of historical significance. The shapes of the character and its writing continued to be changed by several reformers in each century until the period of the nineteenth century. The Tamil characters in the palm leaf manuscripts are based on the author's writing style and strokes in use at the time. Reading palm leaf manuscripts is a challenge for the modern generation who are unaware of the character strokes and writing patterns written in earlier times, and the younger generation neglects to read and understand the contents written in palm leaf manuscripts. To read Tamil palm leaf manuscripts it is necessary to remember the shapes of the character that has changed over time and to compare and recognize the characters. This research paper explains how to recognize the Tamil characters written in palm leaf manuscripts by computer. By this research, the Tamil characters can be compared with different strokes and shapes and the exact character can be recognized accurately and quickly.
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Lin, Cheng-Jian, Yu-Cheng Liu, and Chin-Ling Lee. "Automatic Receipt Recognition System Based on Artificial Intelligence Technology." Applied Sciences 12, no. 2 (January 14, 2022): 853. http://dx.doi.org/10.3390/app12020853.
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In this study, an automatic receipt recognition system (ARRS) is developed. First, a receipt is scanned for conversion into a high-resolution image. Receipt characters are automatically placed into two categories according to the receipt characteristics: printed and handwritten characters. Images of receipts with these characters are preprocessed separately. For handwritten characters, template matching and the fixed features of the receipts are used for text positioning, and projection is applied for character segmentation. Finally, a convolutional neural network is used for character recognition. For printed characters, a modified You Only Look Once (version 4) model (YOLOv4-s) executes precise text positioning and character recognition. The proposed YOLOv4-s model reduces downsampling, thereby enhancing small-object recognition. Finally, the system produces recognition results in a tax declaration format, which can upload to a tax declaration system. Experimental results revealed that the recognition accuracy of the proposed system was 80.93% for handwritten characters. Moreover, the YOLOv4-s model had a 99.39% accuracy rate for printed characters; only 33 characters were misjudged. The recognition accuracy of the YOLOv4-s model was higher than that of the traditional YOLOv4 model by 20.57%. Therefore, the proposed ARRS can considerably improve the efficiency of tax declaration, reduce labor costs, and simplify operating procedures.
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Arnia, Fitri, Khairun Saddami, and Khairul Munadi. "Moment invariant-based features for Jawi character recognition." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (June 1, 2019): 1711. http://dx.doi.org/10.11591/ijece.v9i3.pp1711-1719.
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<p>Ancient manuscripts written in Malay-Arabic characters, which are known as "Jawi" characters, are mostly found in Malay world. Nowadays, many of the manuscripts have been digitalized. Unlike Roman letters, there is no optical character recognition (OCR) software for Jawi characters. This article proposes a new algorithm for Jawi character recognition based on Hu’s moment as an invariant feature that we call the tree root (TR) algorithm. The TR algorithm allows every Jawi character to have a unique combination of moment. Seven values of the Hu’s moment are calculated from all Jawi characters, which consist of 36 isolated, 27 initial, 27 middle, and 35 end characters; this makes a total of 125 characters. The TR algorithm was then applied to recognize these characters. To assess the TR algorithm, five characters that had been rotated to 90o and 180o and scaled with factors of 0.5 and 2 were used. Overall, the recognition rate of the TR algorithm was 90.4%; 113 out of 125 characters have a unique combination of moment values, while testing on rotated and scaled characters achieved 82.14% recognition rate. The proposed method showed a superior performance compared with the Support Vector Machine and Euclidian Distance as classifier.</p>
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Masuhara, Tsukasa, Hideaki Kawano, Hideaki Orii, and Hiroshi Maeda. "Decorated Character Recognition Employing Modified SOM Matching." Applied Mechanics and Materials 103 (September 2011): 649–57. http://dx.doi.org/10.4028/www.scientific.net/amm.103.649.
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Character recognition is a classical issue which has been devoted by a lot of researchers.Making character recognition system more widely available in natural scene images might open upinteresting possibility to use as an input interface of characters and an annotation method for images.Nevertheless, it is still difficult to recognize all sorts of fonts including decorated characters such ascharacters depicted on signboards. The decorated characters are constructed by using some specialtechniques for attracting viewers' attentions. Therefore, it is hard to obtain good recognition results bythe existingOCRs. In this paper,we propose a newcharacter recognition systemusing SOM. The SOMis employed to extract an essential structure concerning the topology from a character. The extractedtopological structure from each character is used to matching and the recognition is performed on thebasis of the topological matching. Experimental results show the effectiveness of the proposed methodin most forms of characters.
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Chen, Yu Feng, and Gang Yin. "A Method of License Plate Chinese Character Recognition Based on Image Quality." Advanced Materials Research 424-425 (January 2012): 309–13. http://dx.doi.org/10.4028/www.scientific.net/amr.424-425.309.
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This paper presents a method of license plate Chinese character recognition based on image quality for improving the recognition rate of low-resolution characters in license plates. Each subset of license plate Chinese character images with the same fuzzy degree are used to construct a corresponding PCA subspace, then the projection residuals distance between the Chinese character to be recognized and each subspace in the specific subspaces which are selected by character’s fuzzy degree is calculated. The class which corresponds to the subspace with the minimum distance is the recognition result. The experimental results show that the proposed method has higher recognition rate and faster recognition speed comparing to other ones.
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Jehangir, Sardar, Sohail Khan, Sulaiman Khan, Shah Nazir, and Anwar Hussain. "Zernike Moments Based Handwritten Pashto Character Recognition Using Linear Discriminant Analysis." January 2021 40, no. 1 (January 1, 2021): 152–59. http://dx.doi.org/10.22581/muet1982.2101.14.
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This paper presents an efficient Optical Character Recognition (OCR) system for offline isolated Pashto characters recognition. Developing an OCR system for handwritten character recognition is a challenging task because of the handwritten characters vary both in shape and in style and most of the time the handwritten characters also vary among the individuals. The identification of the inscribed Pashto letters becomes even palling due to the unavailability of a standard handwritten Pashto characters database. For experimental and simulation purposes a handwritten Pashto characters database is developed by collecting handwritten samples from the students of the university on A4 sized page. These collected samples are then scanned, stemmed and preprocessed to form a medium sized database that encompasses 14784 handwritten Pashto character images (336 distinguishing handwritten samples for each 44 characters in Pashto script). Furthermore, the Zernike moments are considered as a feature extractor tool for the proposed OCR system to extract features of each individual character. Linear Discriminant Analysis (LDA) is followed as a recognition tool for the proposed recognition system based on the calculated features map using Zernike moments. Applicability of the proposed system is tested by validating it with 10-fold cross-validation method and an overall accuracy of 63.71% is obtained for the handwritten Pashto isolated characters using the proposed OCR system.
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KIM, HANG-JOON, and SANG-KYOON KIM. "ON-LINE RECOGNITION OF CURSIVE KOREAN CHARACTERS USING ART-BASED STROKE CLASSIFICATION (RECOGNITION OF CURSIVE KOREAN CHARACTERS)." International Journal of Pattern Recognition and Artificial Intelligence 10, no. 07 (November 1996): 791–812. http://dx.doi.org/10.1142/s0218001496000463.
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This paper proposes an efficient method for on-line recognition of cursive Korean characters. Strokes, primitive components of Korean characters, are usually warped into a cursive form and classifying them is very difficult. To deal with such cursive strokes, we consider them as a recognition unit and automatically classify them by using an ART-2 neural network. The neural network has the advantage of assembling similar patterns together to form classes in a self-organized manner. This ART-2 stroke classifier contributes to high stroke recognition rate and less recognition time. A database for character recognition is also dynamically constructed with a tree structure, and a new character can be included simply by adding a new sequence to it. Character recognition is achieved by traversing the database with a sequence of recognized strokes and positional relations between the strokes. To verify the performance of the system, we tested it on 17,500 handwritten characters, and obtained a good recognition rate of 96.8% and a speed of 0.52 second per character. This results suggest that the proposed method is pertinent to be put into practical use.
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KUNTE, R. SANJEEV, and R. D. SUDHAKER SAMUEL. "WAVELET DESCRIPTORS FOR RECOGNITION OF BASIC SYMBOLS IN PRINTED KANNADA TEXT." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 02 (March 2007): 351–67. http://dx.doi.org/10.1142/s0219691307001793.
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Optical Character Recognition (OCR) systems have been effectively developed for the recognition of printed characters of non-Indian languages. Efforts are underway for the development of efficient OCR systems for Indian languages, especially for Kannada, a popular South Indian language. We present in this paper an OCR system developed for the recognition of basic characters in printed Kannada text, which can handle different font sizes and font sets. Wavelets that have been progressively used in pattern recognition and on-line character recognition systems are used in our system to extract the features of printed Kannada characters. Neural classifiers have been effectively used for the classification of characters based on wavelet features. The system methodology can be extended for the recognition of other south Indian languages, especially for Telugu.
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CHENG, FANG-HSUAN, and WEN-HSING HSU. "RESEARCH ON CHINESE OCR IN TAIWAN." International Journal of Pattern Recognition and Artificial Intelligence 05, no. 01n02 (June 1991): 139–64. http://dx.doi.org/10.1142/s0218001491000107.
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This paper describes typical research on Chinese optical character recognition in Taiwan. Chinese characters can be represented by a set of basic line segments called strokes. Several approaches to the recognition of handwritten Chinese characters by stroke analysis are described here. A typical optical character recognition (OCR) system consists of four main parts: image preprocessing, feature extraction, radical extraction and matching. Image preprocessing is used to provide the suitable format for data processing. Feature extraction is used to extract stable features from the Chinese character. Radical extraction is used to decompose the Chinese character into radicals. Finally, matching is used to recognize the Chinese character. The reasons for using strokes as the features for Chinese character recognition are the following. First, all Chinese characters can be represented by a combination of strokes. Second, the algorithms developed under the concept of strokes do not have to be modified when the number of characters increases. Therefore, the algorithms described in this paper are suitable for recognizing large sets of Chinese characters.
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CHEY, C., P. KUMHOM, and K. CHAMNONGTHAI. "KHMER PRINTED CHARACTER RECOGNITION BY USING WAVELET DESCRIPTORS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14, no. 03 (June 2006): 337–50. http://dx.doi.org/10.1142/s0218488506004047.
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In Khmer printed characters, same character has various shapes according to the fonts and some characters are very similar in shape. In this paper we try to solve these problems, and propose a method of Khmer printed character recognition by using Wavelet Descriptors. In the recognition, firstly the Khmer printed character images are converted to skeleton forms, then skeletons of Khmer character are converted to temporal domain. The templates are obtained by wavelet coefficients from the character training set. To match the input characters with templates, the character recognition method using deformable wavelet descriptor is adapted by using fixed template and Euclidean distance classifier for matching. The smallest distance is the recognition result of the proposed method. As a result, the deformation can be skipped because it might get low recognition rate of similar characters. The experiment consists of two parts. The first part is to evaluate the overall recognition rate of input characters with three different sizes (22-point, 18-point and 12-point) from 10 different fonts of Khmer printed character. Twenty styles of characters are used as the training set. The results show 92.85, 91.66, and 89.27 percent for 22-point, 18-point, and 12-point respectively. The second part is to specifically evaluate the system, testing with one document that has 21 pages of Khmer printed character with different resolutions from a scanner and facsimile (fax). The document is initially printed with 300 dpi (dots per inch), then scanned with three different resolutions, 600 dpi, 300 dpi and 150 dpi. The document that received from fax machine is scanned by 300 dpi. The results show 92.99, 88.61, and 80.05 percent recognition rate for 300, 150 dpi resolutions, and input from fax respectively.
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BOURBAKIS, N. G., and A. T. GUMAHAD. "KNOWLEDGE-BASED RECOGNITION OF TYPED TEXT CHARACTERS." International Journal of Pattern Recognition and Artificial Intelligence 05, no. 01n02 (June 1991): 293–309. http://dx.doi.org/10.1142/s021800149100017x.
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The efficient recognition of typed characters is one of the interesting topics of research with significant application merit in the computerized administration and library domains. In this paper, we present the results of a non-conventional text character extraction technique, called the Horizontal–Vertical Projections (HVP) method. The development of a knowledge base by using Fourier Transform as a tool which creates the characters' main features for the knowledge base, improves the performance of the HVP as a text character recognition technique. Various problems and their solutions are discussed with the presentation of the knowledge-based character recognition process.
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Shen, Chuanxing, Yongjian Zhu, and Chi Wang. "Research on Character Computer Intelligent Recognition of Automobile Front Bar Bracket Based on Machine Vision." Journal of Physics: Conference Series 2083, no. 4 (November 1, 2021): 042041. http://dx.doi.org/10.1088/1742-6596/2083/4/042041.
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Abstract Aiming at the problem that the characters in the superimposed character area on the surface of the front bumper bracket of the car are difficult to recognize, a machine vision-based automobile front bumper bracket character recognition method is proposed, use Python, OpenCV and Halcon computer vision library for software system design. For eight images collected from different angles of light source directions, an innovative method of polynomial fitting gray value can reducethe uneven illumination of the images. The photometric stereo algorithm is used to obtain a high-contrast character image, and the separation of the two types of characters is simultaneously achieved. After the image filtering, opening and closing processes canremove background interference, use a morphological improvement algorithm based on scanning algorithm to complete character positioning, and then the improved algorithm of projection dichotomy based on the connected domain size is used to complete the character segmentation, and finally the support vector machine is used for character feature recognition. The experimental results claim that these methods can quickly separate superimposed characters and complete the recognition of non-color difference convex characters and inkjet characters, and the average recognition accuracy rate is over 96%, which meets the expected recognition requirements.
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Al-Yousefi, H., and S. S. Udpa. "Recognition of Arabic characters." IEEE Transactions on Pattern Analysis and Machine Intelligence 14, no. 8 (1992): 853–57. http://dx.doi.org/10.1109/34.149585.
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Wu, Wei, Zheng Liu, Mo Chen, Zhiming Liu, Xi Wu, and Xiaohai He. "A New Framework for Container Code Recognition by Using Segmentation-Based and HMM-Based Approaches." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 01 (January 4, 2015): 1550004. http://dx.doi.org/10.1142/s0218001415500044.
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Traditional methods for automatic recognition of container code in visual images are based on segmentation and recognition of isolated characters. However, when the segment fails to separate each character from the others, those methods will not function properly. Sometimes the container code characters are printed or arranged very closely, which makes it a challenge to isolate each character. To address this issue, a new framework for automatic container code recognition (ACCR) in visual images is proposed in this paper. In this framework, code-character regions are first located by applying a horizontal high-pass filter and scan line analysis. Then, character blocks are extracted from the code-character regions and further classified into two categories, i.e. single-character block and multi-character block. Finally, a segmentation-based approach is implemented for recognition of the characters in single-character blocks, and a hidden Markov model (HMM)-based method is proposed for the multi-character blocks. The experimental results demonstrate the effectiveness of the proposed method, which can successfully recognize the container code with closely arranged characters.
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Nikam, Saurabh Ravindra. "Character Segmentation and Recognition of Marathi Language." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 1544–51. http://dx.doi.org/10.22214/ijraset.2021.39566.
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Abstract: In this paper Segmentation is one the most important process which decides the success of character recognition fashion. Segmentation is used to putrefy an image of a sequence of characters into sub images of individual symbols by segmenting lines and words. In segmentation image is partitioned into multiple corridor. With respect to the segmentation of handwritten words into characters it's a critical task because of complexity of structural features and kinds in writing styles. Due to this without segmentation these touching characters, it's delicate to fete the individual characters, hence arises the need for segmentation of touching characters in a word. Then we consider Marathi words and Marathi Numbers for segmentation. The algorithm is use for Segmentation of lines and also characters. The segmented characters are also stores in result variable. First it Separate the lines and also it Separate the characters from the input image. This procedure is repeated till end of train. Keywords: Image Segmentation, Handwritten Marathi Characters, Marathi Numbers, OCR.
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Liao, Ching-Chih. "Double-Sided Occluded Chinese Character Recognition Accuracy and Response Time for Design and Nondesign Educational Background." SAGE Open 8, no. 4 (October 2018): 215824401881006. http://dx.doi.org/10.1177/2158244018810065.
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This article investigates the influence of the position of occlusion, structural composition, and design educational status on Chinese character recognition accuracy and response time. Tsao and Liao conducted an experiment using 18 of the 4,000 most commonly used Chinese characters and suggested that the primary and secondary recognition features of a “single-sided” occluded Chinese character are the key radical (or initial strokes) and the key component (i.e., combination of strokes), respectively. The study concluded that right-side occluded characters require a shorter response time and yield more accurate recognition and that educational background does not significantly affect recognition accuracy and response time. The present study considered the same 18 Chinese characters and extended the work of Tsao and Liao by exploring accuracy rate and response time in design and nondesign educational groups for the recognition of “double-sided” occluded Chinese characters. The experimental results indicated that right-side occlusion (including both bottom-right and top-right occlusion) requires a shorter response time and yields more accurate recognition than left-side occlusion. These results agree with those of Tsao and Liao, who found that the key radical of a Chinese character is its key visual recognition feature. Even double-sided occlusion of Chinese characters does not affect the recognition outcome if the position of occlusion does not blur the key radical. Moreover, the participants majoring in design recognized the occluded Chinese characters more slowly than those with no educational background in design.
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TAN, JUN, XIAOHUA XIE, WEI-SHI ZHENG, and JIAN-HUANG LAI. "RADICAL EXTRACTION USING AFFINE SPARSE MATRIX FACTORIZATION FOR PRINTED CHINESE CHARACTERS RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 03 (May 2012): 1250005. http://dx.doi.org/10.1142/s021800141250005x.
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Each Chinese character is comprised of radicals, where a single character (compound character) contains one (or more than one) radicals. For human cognitive perspective, a Chinese character can be recognized by identifying its radicals and their spatial relationship. This human cognitive law may be followed in computer recognition. However, extracting Chinese character radicals automatically by computer is still an unsolved problem. In this paper, we propose using an improved sparse matrix factorization which integrates affine transformation, namely affine sparse matrix factorization (ASMF), for automatically extracting radicals from Chinese characters. Here the affine transformation is vitally important because it can address the poor-alignment problem of characters that may be caused by internal diversity of radicals and image segmentation. Consequently we develop a radical-based Chinese character recognition model. Because the number of radicals is much less than the number of Chinese characters, the radical-based recognition performs a far smaller category classification than the whole character-based recognition, resulting in a more robust recognition system. The experiments on standard Chinese character datasets show that the proposed method gets higher recognition rates than related Chinese character recognition methods.
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MURASE, HIROSHI. "ONE-LINE RECOGNITION SYSTEM FOR FREE-FORMAT HANDWRITTEN JAPANESE CHARACTERS." International Journal of Pattern Recognition and Artificial Intelligence 05, no. 01n02 (June 1991): 207–20. http://dx.doi.org/10.1142/s0218001491000144.
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This paper describes an on-line recognition system for free-format handwritten Japanese character strings which may contain characters with separated constituents or overlapping characters. The recognition method for the system, called candidate lattice method, conducts segmentation and recognition of individual character candidates, and applies linguistic information to determine the most probable character string in order to achieve high recognition rates. Special hardware designed to realize a real-time recognition system is also introduced. The method used on the special hardware attained a segmentation rate of 98.8% and an overall recognition rate of 98.7% for 105 samples.
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Rani, Vneeta, and Dr Vijay Laxmi. "Segmentation of Handwritten Text Document Written in Devanagri Script for Simple character, skewed character and broken character." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 8, no. 1 (June 20, 2013): 686–91. http://dx.doi.org/10.24297/ijct.v8i1.3427.
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OCR (optical character recognition) is a technology that is commonly used for recognizing patterns artificial intelligence & computer machine. With the help of OCR we can convert scanned document into editable documents which can be further used in various research areas. In this paper, we are presenting a character segmentation technique that can segment simple characters, skewed characters as well as broken characters. Character segmentation is very important phase in any OCR process because output of this phase will be served as input to various other phase like character recognition phase etc. If there is some problem in character segmentation phase then recognition of the corresponding character is very difficult or nearly impossible.
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FU, HSIN-CHIA, Y. Y. XU, and H. Y. CHANG. "RECOGNITION OF HANDWRITTEN SIMILAR CHINESE CHARACTERS BY SELF-GROWING PROBABILISTIC DECISION-BASED NEURAL NETWORK." International Journal of Neural Systems 09, no. 06 (December 1999): 545–61. http://dx.doi.org/10.1142/s0129065799000575.
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Recognition of similar (confusion) characters is a difficult problem in optical character recognition (OCR). In this paper, we introduce a neural network solution that is capable of modeling minor differences among similar characters, and is robust to various personal handwriting styles. The Self-growing Probabilistic Decision-based Neural Network (SPDNN) is a probabilistic type neural network, which adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme. Based on the SPDNN model, we have constructed a three-stage recognition system. First, a coarse classifier determines a character to be input to one of the pre-defined subclasses partitioned from a large character set, such as Chinese mixed with alphanumerics. Then a character recognizer determines the input image which best matches the reference character in the subclass. Lastly, the third module is a similar character recognizer, which can further enhance the recognition accuracy among similar or confusing characters. The prototype system has demonstrated a successful application of SPDNN to similar handwritten Chinese recognition for the public database CCL/HCCR1 (5401 characters × 200 samples). Regarding performance, experiments on the CCL/HCCR1 database produced 90.12% recognition accuracy with no rejection, and 94.11% accuracy with 6.7% rejection, respectively. This recognition accuracy represents about 4% improvement on the previously announced performance.5,11 As to processing speed, processing before recognition (including image preprocessing, segmentation, and feature extraction) requires about one second for an A4 size character image, and recognition consumes approximately 0.27 second per character on a Pentium-100 based personal computer, without use of any hardware accelerator or co-processor.
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Chen, Ju-Wei, and Suh-Yin Lee. "On-Line Chinese Character Recognition Via a Representation of Spatial Relationships Between Strokes." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 03 (May 1997): 329–57. http://dx.doi.org/10.1142/s0218001497000159.
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Chinese characters are constructed by basic strokes based on structural rules. In handwritten characters, the shapes of the strokes may vary to some extent, but the spatial relations and geometric configurations of the strokes are usually maintained. Therefore these spatial relations and configurations could be regarded as invariant features and could be used in the recognition of handwritten Chinese characters. In this paper, we investigate the structural knowledge in Chinese characters and propose the stroke spatial relationship representation (SSRR) to describe Chinese characters. An On-Line Chinese Character Recognition (OLCCR) method using the SSRR is also presented. With SSRR, each character is processed and is represented by an attribute graph. The process of character recognition is thereby transformed into a graph matching problem. After careful analysis, the basic spatial relationship between strokes can be characterized into five classes. A bitwise representation is adopted in the design of the data structure to reduce storage requirements and to speed up character matching. The strategy of hierarchical search in the preclassification improves the recognition speed. Basically, the attribute graph model is a generalized character representation that provides a useful and convenient representation for newly added characters in an OLCCR system with automatic learning capability. The significance of the structural approach of character recognition using spatial relationships is analyzed and is proved by experiments. Realistic testing is provided to show the effectiveness of the proposed method.
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Miyao, Hidetoshi, Yasuaki Nakano, Atsuhiko Tani, Hirosato Tabaru, and Toshihiro Hananoi. "Printed Japanese Character Recognition Using Multiple Commercial OCRs." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 2 (March 20, 2004): 200–207. http://dx.doi.org/10.20965/jaciii.2004.p0200.
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This paper proposes two algorithms for maintaining matching between lines and characters in text documents output by multiple commercial optical character readers (OCRs). (1) a line matching algorithm using dynamic programming (DP) matching and (2) a character matching algorithm using character string division and standard character strings. The paper proposes a method that introduces majority logic and reject processing in character recognition. To demonstrate the feasibility of the method, we conducted experiments on line matching recognition for 127 document images using five commercial OCRs. Results demonstrated that the method extracted character areas with more accuracy than a single OCR along with appropriate line matching. The proposed method enhanced recognition from 97.61% provided by a single OCR to 98.83% in experiments using the character matching algorithm and character recognition. This method is expected to be highly useful in correcting locations at which unwanted lines or characters occur or required lines or characters disappear.
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Liu, Ying Jie, and Fu Cheng You. "Application of Mathematical Morphology on Touching or Broken Characters Processing." Advanced Materials Research 171-172 (December 2010): 73–77. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.73.
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It is difficult to process touching or broken characters in practical applications on optical character recognition. For touching or broken characters, a method based on mathematical morphology of binary image is put forward in the paper. On the basis of the relative theories of digital image processing, the overall process is introduced including separation of touching characters and connection of broken characters. First of all, character image is pre-processed through smoothing and threshold segmentation in order to generate binary image of characters. Then character regions which are touching or broken are processed through different operators of mathematical morphology of binary image by different structuring elements. Thus the touching characters are separated and broken characters are connected. For higher recognition rate, further processes are done to achieve normal and individual character regions.
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Sharma, Kartik, S. V. Jagadeesh Kona, Anshul Jangwal, Aarthy M, Prayline Rajabai C, and Deepika Rani Sona. "Handwritten Digits and Optical Characters Recognition." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4 (May 4, 2023): 20–24. http://dx.doi.org/10.17762/ijritcc.v11i4.6376.
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The process of transcribing a language represented in its spatial form of graphical characters into its symbolic representation is called handwriting recognition. Each script has a collection of characters or letters, often known as symbols, that all share the same fundamental shapes. Handwriting analysis aims to correctly identify input characters or images before being analysed by various automated process systems. Recent research in image processing demonstrates the significance of image content retrieval. Optical character recognition (OCR) systems can extract text from photographs and transform that text to ASCII text. OCR is beneficial and essential in many applications, such as information retrieval systems and digital libraries.
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HUANG, JUN S., and PEI-MING HUANG. "MACHINE-PRINTED CHINESE CHARACTER RECOGNITION BASED ON LINEAR REGRESSION." International Journal of Pattern Recognition and Artificial Intelligence 05, no. 01n02 (June 1991): 165–73. http://dx.doi.org/10.1142/s0218001491000119.
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Segmented machine-printed Chinese characters generally suffer from small distortions and small rotations due to noise and segmentation errors. These phenomena cause many conventional methods, especially those based on directional codes, to be unable to reach very high recognition rates, say above 99%. In this paper, regressional analysis is proposed as a means to overcome these problems. Firstly, thinning is applied to each segmented character, which is enclosed in a proper square box and also filtered for noise reduction beforehand. Secondly, the square thinned character image is divided into 9×9 meshes (blocks), instead of the conventional 8×8, for reasons of the Chinese character's characteristics and also for global feature extraction. Thirdly, line regression is applied, for all black points in each block, to obtain either the value of the slope angle, or a dispersion code which is derived from the sample correlation coefficient after proper transformation. Thus, each block is coded by one of three cases: 'blank', value of slope angle, or 'dispersion'. The peripheral blacks are used for preclassification. Proper scores for matching two characters are designed so that learning and recognition are quite efficient. The objective of designing this optical character recognition system is to get very small misrecognition rates and tolerable rejection rates. Experiments with three fonts, each consisting of 5401 characters, were carried out. The overall rejection rate is 1.25% and the overall misrecognition rate is 0.33%. These are acceptable for most users.
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Dr. M.V. Vijaya Saradhi, K. Rakesh, D. Ravi Prasanna, K. Swetha, and B. Praveen. "COMPREHENSIVE STUDY OF DEEP LEARNING BASED TELUGU OCR." international journal of engineering technology and management sciences 7, no. 3 (2023): 874–77. http://dx.doi.org/10.46647/ijetms.2023.v07i03.133.
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The aim of the project is to understand offline One of the most popular and difficult pattern recognition subjects is the use of optical character recognition (OCR) to read handwritten Telugu letters. This study suggests a three-stage OCR solution for Telugu documents that includes pre-processing, feature extraction, and classification. For the extraction of boundary edge pixel points during preprocessing, we used median filtering on the input characters as well as normalisation and skeletonization techniques. Each character is initially divided into three 3x3 grids during the feature extraction stage, and the associated centroid for each of the nine zones is assessed. This allows us to recognise characters in various styles. Following that, we drew the projection angel's horizontal and vertical symmetry to the character's closest pixel.
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Chang, Yasheng, and Weiku Wang. "Text recognition in radiographic weld images." Insight - Non-Destructive Testing and Condition Monitoring 61, no. 10 (October 1, 2019): 597–602. http://dx.doi.org/10.1784/insi.2019.61.10.597.
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Automatic recognition of text characters on radiographic images based on computer vision would be a very useful step forward as it could improve and simplify the file handling of digitised radiographs. Text recognition in radiographic weld images is challenging since there is no uniform font or character size and each character may tilt in different directions and by different amounts. Deep learning approaches for text recognition have recently achieved breakthrough performance using convolutional neural networks (CNNs). CNNs can recognise normalised characters in different fonts. However, the tilt of a character still has a strong influence on the accuracy of recognition. In this paper, a new improved algorithm is proposed based on the Radon transform, which is very effective at character rectification. The improved algorithm increases the accuracy of character recognition from 86.25% to 98.48% in the current experiments. The CNN is used to recognise the rectified characters, which achieves good accuracy and improves character recognition in radiographic weld images. A CNN greatly improves the efficiency of digital scanning and filing of radiographic film. The method proposed in this paper is also compared with other methods that are commonly used in other fields and the results show that the proposed method is better than state-of-the-art methods.
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Liu, Yanchi, Shijia Zhang, Yuman Zhang, Qiuping Cheng, Yujie Chen, and Lei Mo. "Neighborhood frequency effect on Chinese character recognition: An investigation of lexical decision tasks." Social Behavior and Personality: an international journal 51, no. 2 (February 8, 2023): 1–9. http://dx.doi.org/10.2224/sbp.12121.
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Many studies have found that the orthographic neighborhood frequency (NF) effect plays a dominant role in word identification. Yet most research has been conducted on alphabetic languages rather than Chinese. We investigated the NF effect on Chinese character recognition in the context of lexical decision tasks. Experiment 1 tested the NF effect in simple characters, Experiment 2 tested the NF effect in compound characters. Results showed that targets with higher frequency neighbors had longer response latencies for both simple characters and compound characters, and that this inhibitory effect was more significant for low-frequency targets. The results overall imply there is an inhibitory NF effect existing in Chinese character recognition. The implications of the results are discussed with regard to character recognition.
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Inoue, Hiroyuki, and Yuma Fujisaki. "Impression Space Analysis of Local Mascot Characters for Regional Promotion." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 5 (September 20, 2018): 731–37. http://dx.doi.org/10.20965/jaciii.2018.p0731.
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Recently, local mascot characters called “Yurukyara” have been active in various places. They play an important role in raising a region’s image and exciting the region to promote regional development. It is important to understand the impression given by the characters, since each character’s image leads to the promotion and recognition of the region. In this study, we analyze the impression of local mascot characters to provide useful information for regional promotions, etc. First, we extract the Kansei factors from the characters’ appearances and classify the characters within the factor space. Next, we analyze the differences in impressions when adding character-profile and video information.
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Shitu, Saifullahi Sadi, Syed Abd Rahman Syed Abu Bakar, Nura Musa Tahir, Usman Isyaku Bature, and Haliru Liman. "Efficient Thinning Algorithm for Malaysian Car Plate Character Recognition." ELEKTRIKA- Journal of Electrical Engineering 20, no. 3 (December 27, 2021): 15–25. http://dx.doi.org/10.11113/elektrika.v20n3.286.
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The thinning algorithm is one of the approaches of identifying each character printed on the car plate. Malaysian car plate characters appear in different character sizes, styles, customized printed characters etc. These variations contribute to difficulty in thinning successfully segmented and extracted license plate characters for recognition. To address these problems, an improved thinning operation for Malaysian car plate character recognition is proposed. In this algorithm, samples from segmented and extracted license plates are used for a thinning operation which is passed to Zhang-Suen thinning algorithm that could not guarantee one pixel thick and then to single pixelate algorithm that provides one pixel width of character for recognition. From the simulation, the result obtained has clearly proven to be the best for character recognition systems with least number of white pixels (777 pixels) and 0.26% redundant pixel left in the medial curve.
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Mustafa, Yusron Farid, Farid Ridho, and Siti Mariyah. "Study of Handwriting Recognition Implementation in Data Entry of Survei Angkatan Kerja Nasional (SAKERNAS) using CNN." Proceedings of The International Conference on Data Science and Official Statistics 2021, no. 1 (January 4, 2022): 53–65. http://dx.doi.org/10.34123/icdsos.v2021i1.32.
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The use of Paper and Pencil Interviewing (PAPI) at BPS requires manual data entry that cannot be separated from the human ability to recognize handwriting. For computers, handwriting recognition is complex work that requires complex algorithms. Convolutional Neural Network (CNN) is an algorithm that can accommodate the complexity of handwriting recognition. This research intends to conduct a study on the implementation of the handwriting recognition model using CNN in recognizing handwriting on the PAPI questionnaire in data entry activities. Handwriting recognition model was built using the EMNIST dataset separately according to its character type and provides 89% accuracy for characters in the form of letters and numbers, 95% for characters in the form of letters, and 99% for characters in the form of numbers. Implementation of the handwriting recognition on the questionnaire image shows good results with 83.33% accuracy. However, there are problems found in the process of character segmentation where characters are not segmented correctly because the line of writing continues on the character that should be separated and disconnected characters when they should be joined. The result obtained in this study is expected to be a consideration regarding the entry method data used by BPS later.
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Ajao, Jumoke Falilat, David Olufemi Olawuyi, and Odetunji Ode Odejobi. "Yoruba Handwritten Character Recognition using Freeman Chain Code and K-Nearest Neighbor Classifier." Jurnal Teknologi dan Sistem Komputer 6, no. 4 (October 31, 2018): 129–34. http://dx.doi.org/10.14710/jtsiskom.6.4.2018.129-134.
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This work presents a recognition system for Offline Yoruba characters recognition using Freeman chain code and K-Nearest Neighbor (KNN). Most of the Latin word recognition and character recognition have used k-nearest neighbor classifier and other classification algorithms. Research tends to explore the same recognition capability on Yoruba characters recognition. Data were collected from adult indigenous writers and the scanned images were subjected to some level of preprocessing to enhance the quality of the digitized images. Freeman chain code was used to extract the features of THE digitized images and KNN was used to classify the characters based on feature space. The performance of the KNN was compared with other classification algorithms that used Support Vector Machine (SVM) and Bayes classifier for recognition of Yoruba characters. It was observed that the recognition accuracy of the KNN classification algorithm and the Freeman chain code is 87.7%, which outperformed other classifiers used on Yoruba characters.
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Lin, Weiwei, Tai Ma, Zeqing Zhang, Xiaofan Li, and Xingsi Xue. "Variational Autoencoder for Zero-Shot Recognition of Bai Characters." Wireless Communications and Mobile Computing 2022 (July 4, 2022): 1–8. http://dx.doi.org/10.1155/2022/2717322.
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When talking about Bai nationality, people are impressed by its long history and the language it has created. However, since fewer people of the young generation learn the traditional language, the glorious Bai culture becomes less known, making understanding Bai characters difficult. Based on the highly precise character recognition model for Bai characters, the paper is aimed at helping people read books written in Bai characters so as to popularize the culture. To begin with, a data set is built with the support of Bai culture fans and experts. However, the data set is not large enough as knowledge in this respect is limited. This makes the deep learning model less accurate since it lacks sufficient data. The popular zero-shot learning (ZSL) is adopted to overcome the insufficiency of data sets. We use Chinese characters as the seen class, Bai characters as the unseen class, and the number of strokes as the attribute to construct the ZSL format data set. However, the existing ZSL methods ignore the character structure information, so a generation method based on variational autoencoder (VAE) is put forward, which can automatically capture the character structure information. Experimental results show that the method facilitates the recognition of Bai characters and makes it more precise.
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AMIN, ADNAN, CLAUDE SAMMUT, and K. C. SUM. "LEARNING TO RECOGNIZE HAND-PRINTED CHINESE CHARACTERS USING INDUCTIVE LOGIC PROGRAMMING." International Journal of Pattern Recognition and Artificial Intelligence 10, no. 07 (November 1996): 829–47. http://dx.doi.org/10.1142/s0218001496000487.
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Recognition of Chinese characters has been a major interest of researchers for many years, and a large number of research papers and reports have already been published in this area. There are several major problems: Chinese characters are distinct and ideographic, the character size is very large and a lot of structurally similar characters exist in the character set. Thus, classification criteria are difficult to find. This paper presents a new technique for the recognition of hand-printed Chinese characters using machine learning. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variations in writing styles. The advantages of machine learning are twofold: it can generalize over the large degree of variations between writing styles and recognition rules can be constructed by example. The paper also describes three methods of feature extraction for Chinese character recognition: regular expression, dominant point and modified Hough transform. These methods are then compared in terms of accuracy and efficiency.
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Yadav, Bharati, Ajay Indian, and Gaurav Meena. "HDevChaRNet: A deep learning-based model for recognizing offline handwritten devanagari characters." Journal of Autonomous Intelligence 6, no. 2 (August 15, 2023): 679. http://dx.doi.org/10.32629/jai.v6i2.679.
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<p>Optical character recognition (OCR) converts text images into machine-readable text. Due to the non-availability of several standard datasets of Devanagari characters, researchers have used many techniques for developing an OCR system with varying recognition rates using their own created datasets. The main objective of our proposed study is to improve the recognition rate by analyzing the effect of using batch normalization (BN) instead of dropout in convolutional neural network (CNN) architecture. So, a CNN-based model HDevChaRNet (Handwritten Devanagari Character Recognition Network) is proposed in this study for same to recognize offline handwritten Devanagari characters using a dataset named Devanagari handwritten character dataset (DHCD). DHCD comprises a total of 46 classes of characters, out of which 36 are consonants, and 10 are numerals. The proposed models based on convolutional neural network (CNN) with BN for recognizing the Devanagari characters showed an improved accuracy of 98.75%, 99.70%, and 99.17% for 36, 10, and 46 classes, respectively.</p>
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Amin, Muhammad Sadiq, Siddiqui Muhammad Yasir, and Hyunsik Ahn. "Recognition of Pashto Handwritten Characters Based on Deep Learning." Sensors 20, no. 20 (October 17, 2020): 5884. http://dx.doi.org/10.3390/s20205884.
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Handwritten character recognition is increasingly important in a variety of automation fields, for example, authentication of bank signatures, identification of ZIP codes on letter addresses, and forensic evidence. Despite improved object recognition technologies, Pashto’s hand-written character recognition (PHCR) remains largely unsolved due to the presence of many enigmatic hand-written characters, enormously cursive Pashto characters, and lack of research attention. We propose a convolutional neural network (CNN) model for recognition of Pashto hand-written characters for the first time in an unrestricted environment. Firstly, a novel Pashto handwritten character data set, “Poha”, for 44 characters is constructed. For preprocessing, deep fusion image processing techniques and noise reduction for text optimization are applied. A CNN model optimized in the number of convolutional layers and their parameters outperformed common deep models in terms of accuracy. Moreover, a set of benchmark popular CNN models applied to Poha is evaluated and compared with the proposed model. The obtained experimental results show that the proposed model is superior to other models with test accuracy of 99.64 percent for PHCR. The results indicate that our model may be a strong candidate for handwritten character recognition and automated PHCR applications.
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Zhang, Yan, and Liumei Zhang. "SGooTY: A Scheme Combining the GoogLeNet-Tiny and YOLOv5-CBAM Models for Nüshu Recognition." Electronics 12, no. 13 (June 26, 2023): 2819. http://dx.doi.org/10.3390/electronics12132819.
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With the development of society, the intangible cultural heritage of Chinese Nüshu is in danger of extinction. To promote the research and popularization of traditional Chinese culture, we use deep learning to automatically detect and recognize handwritten Nüshu characters. To address difficulties such as the creation of a Nüshu character dataset, uneven samples, and difficulties in character recognition, we first build a large-scale handwritten Nüshu character dataset, HWNS2023, by using various data augmentation methods. This dataset contains 5500 Nüshu images and 1364 labeled character samples. Second, in this paper, we propose a two-stage scheme model combining GoogLeNet-tiny and YOLOv5-CBAM (SGooTY) for Nüshu recognition. In the first stage, five basic deep learning models including AlexNet, VGGNet16, GoogLeNet, MobileNetV3, and ResNet are trained and tested on the dataset, and the model structure is improved to enhance the accuracy of recognising handwritten Nüshu characters. In the second stage, we combine an object detection model to re-recognize misidentified handwritten Nüshu characters to ensure the accuracy of the overall system. Experimental results show that in the first stage, the improved model achieves the highest accuracy of 99.3% in recognising Nüshu characters, which significantly improves the recognition rate of handwritten Nüshu characters. After integrating the object recognition model, the overall recognition accuracy of the model reached 99.9%.
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Wiguna, I. Komang Arya Ganda, and Agus Muliantara. "Introduction of Balinese Script Handwriting Using Zoning and Multilayer Perceptron." ACSIE (International Journal of Application Computer Science and Informatic Engineering) 1, no. 1 (May 31, 2019): 1–10. http://dx.doi.org/10.33173/acsie.34.
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Handwriting identification is one out of the many research ever conducted. In its development, the handwriting can be written in real time by the user by using the mouse (online character recognition). Various studies on the traditional character handwriting recognition continue to be developed. One of them is the recognition of the Balinese characters. Balinese characters have their own unique characters compared with the other regions. The difference between the shapes of the characters with the other characters are quite similar, or there are some characters that can only be distinguished by a small sketch or doodle.This study uses Artificial Neural Network with Backpropagation algorithm to perform the Balinese characters recognition and zoning as a method of feature extraction. In a variation of the extraction method, the characteristics used are Image Centroid and Zone (ICZ), Zone Centroid and Zone (ZCZ) and normalization of features. Of the three methods, it will be determined the best method used in the Balinese characters recognition.From the test results of the extraction method, the combined characteristics of the ICZ, ZCZ and normalization of features were the most effective to be used for the recognition of the Balinese characters. The level of accuracy obtained from the results of the online testing was 71,28% and 72,31% for offline testing, with parameters of Backpropagation, which used the value of learning rate of 0,03, a momentum value of 0,5 and the number of neurons in the hidden layer of 130.
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Ali, Aree, and Bayan Omer. "Invarianceness for Character Recognition Using Geo-Discretization Features." Computer and Information Science 9, no. 2 (March 17, 2016): 1. http://dx.doi.org/10.5539/cis.v9n2p1.
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<span style="font-size: 10pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 11.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">Recognition rate of characters in the handwritten is still a big challenge for the research because of a shape variation, scale and format in a given handwritten character. A more complicated handwritten character recognition system needs a better feature extraction technique that deal with such variation of hand writing. In other hand, to obtain efficient and accurate recognition rely on off-line English handwriting character, the similarity in the character traits is an important issue to be differentiated in an off-line English handwriting to. In recognizing a character, character handwriting format could be implicitly analyzed to make the representation of the unique hidden features of the individual's character is allowable. Unique features can be used in recognizing characters which can be considerable when the similarity between two characters is high. However, the problem of the similarity in off-line English character handwritten was not taken into account thus, leaving a high possibility of degrading the similarity error for intra-class [same character] with the decrease of the similarity error for inter-class [different character]. Therefore, in order to achieve better performance, this paper proposes a discretization feature algorithm to reduce the similarity error for intra-class [same character]. The mean absolute error is used as a parameter to calculate the similarity between inter and/or intra class characters. Test results show that the identification rate give a better result with the proposed hybrid Geo-Discretization method.</span>
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Guo, Chu Yu, Yuan Yan Tang, Zhen Chao Zhang, Bing Li, and Chang Song Liu. "An OCR Post-Processing Method Based on Dictionary Matching and Matrix Transforming." Applied Mechanics and Materials 427-429 (September 2013): 1861–65. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1861.
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This paper describes a post-processing method for Chinese and Japanese character recognition based on dictionary. By the analysis results of recognition in the processing of OCR, we can find some segmentation and recognition errors do not conform to the rules of lexical and just recognized as the characters which its fonts approach to the scanned texts. For these errors we can deal with them by the Fix Length Segmentation Matching based on Dictionary and the Glyph Code Matrix Transforming. Through the above processing, most of the inaccurate recognitions can be corrected and by the experimental results, it can be proved that this method is an effective way to improve the recognition rate of Chinese and Japanese Character.
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Naidu, D. J. Samatha, and T. Mahammad Rafi. "HANDWRITTEN CHARACTER RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS." International Journal of Computer Science and Mobile Computing 10, no. 8 (August 30, 2021): 41–45. http://dx.doi.org/10.47760/ijcsmc.2021.v10i08.007.
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Handwritten character Recognition is one of the active area of research where deep neural networks are been utilized. Handwritten character Recognition is a challenging task because of many reasons. The Primary reason is different people have different styles of handwriting. The secondary reason is there are lot of characters like capital letters, small letters & special symbols. In existing were immense research going on the field of handwritten character recognition system has been design using fuzzy logic and created on VLSI(very large scale integrated)structure. To Recognize the tamil characters they have use neural networks with the Kohonen self-organizing map(SOM) which is an unsupervised neural networks. In proposed system this project design a image segmentation based hand written character recognition system. The convolutional neural network is the current state of neural network which has wide application in fields like image, video recognition. The system easily identify or easily recognize text in English languages and letters, digits. By using Open cv for performing image processing and having tensor flow for training the neural network. To develop this concept proposing the innovative method for offline handwritten characters. detection using deep neural networks using python programming language.
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Suthar, Sanket B., and Amit R. Thakkar. "CNN-Based Optical Character Recognition for Isolated Printed Gujarati Characters and Handwritten Numerals." International Journal of Mathematical, Engineering and Management Sciences 7, no. 5 (October 1, 2022): 643–55. http://dx.doi.org/10.33889/ijmems.2022.7.5.042.
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Optical character recognition (OCR) technologies have made significant progress in the field of language recognition. Gujarati is a more difficult language to recognize compared to other languages because of curves, close loops, the inclusion of modifiers, and the presence of joint characters. So great effort has been laid into the literature for Gujarati OCR. Recently deep learning-based CNN models are applied to develop OCR for different languages but Convolutional Neural Networks (CNN) models are not yet giving a satisfactory performance to recognize Gujarati characters. So, this paper proposes a revolutionary Gujarati printed characters and numerals recognition CNN models. CNN-PGC (CNN for - Printed Gujarati Character) and CNN-HGC (CNN for - Handwritten Gujarati Character) are two optimally configured Convolutional Neural Networks (CNNs) presented in this research for printed Gujarati base characters and handwritten numbers, respectively. Concerning particular performance indicators, the suggested work's performance is evaluated and proven against that of other traditional models and with the latest baseline methods. Experimental analysis has been carried out on well-segmented newly generated Gujarati base characters and numerals dataset which includes 36 consonants, 13 vowels, and 10 handwritten numerals. Variation in the database is also taken into consideration during experiments like size, skew, noise blue, etc. Even in the presence of printing irregularities, writing irregularities, and degradations the proposed method achieves a 98.08% recognition rate for print characters and a 95.24 % recognition rate for handwritten numerals which is better than other existing models.
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Li, Ling Hua, Shou Fang Mi, and Heng Bo Zhang. "Template-Based Handwritten Numeric Character Recognition." Advanced Materials Research 586 (November 2012): 384–88. http://dx.doi.org/10.4028/www.scientific.net/amr.586.384.
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This paper describes a stroke-based handwriting analysis method in classifying handwritten Numeric characters by using a template-based approach. Writing strokes are variable from time to time, even when the writing character is same and comes from the same user. Writing strokes include the properties such as the number of the strokes, the shapes and sizes of them and the writing order and the writing speed. We describe here a template-based system using the properties of writing strokes for the recognition of online handwritten numeric characters. Experimental results show that within the 1500 numeric characters taken from 30 writers, the system got 97.84% recognition accuracy which is better than other systems shown by other literatures.