Journal articles on the topic 'Character recogniion'
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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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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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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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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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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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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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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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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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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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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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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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Nogra, James Arnold, Cherry Lyn Sta Romana, and Elmer Maravillas. "Baybáyin Character Recognition Using Convolutional Neural Network." International Journal of Machine Learning and Computing 10, no. 2 (February 2020): 265–70. http://dx.doi.org/10.18178/ijmlc.2020.10.2.930.
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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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Dikmonienė, Jovita. "Anagnorisis in Aristotle’s Poetics: problems of definition and classification." Literatūra 61, no. 3 (December 20, 2019): 32–41. http://dx.doi.org/10.15388/litera.2019.3.3.
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The article analyses the problems of meaning and classification of the term anagnorisis (ἀναγνώρισις) as it is defined in Aristotle’s Poetics. It focuses on how the term anagnorisis is understood and interpreted by scholars – different translations and their interpretations of the same type of anagnorisis are compared. The article also searches for the answers to the following questions: does the term of anagnorisis discussed by Aristotle mean the recognition of persons or just any kind of truth in a drama; why do some translators differentiate five and others six types of anagnorisis; what did Aristotle bear in mind by distinguishing the type of anagnorisis called “the recognition made by a poet himself” (Arist. Poet. XVI, 1454b 30–31), whereas it is known that all recognitions were created by poets themselves; does “an anagnorisis by false reasoning (a false syllogism)” occur among tragedy characters or does the audience at first misjudge, but later recognises the characters correctly?The author of the article argues that the version of the Arabic manuscript of Aristotle’s Poetics is more logical, as it states that one of the characters (θατέρου) rather than a spectator (θεάτρου) mistakenly recognises another character (Arist. Poet. XVI, 1455a 12–17). First of all, Aristotle does not state specifically that this is the fifth (different from all the others) way of recognition, but while discussing the fourth way of “anagnorisis by reasoning”, he adds that there is also and “an anagnorisis by false reasoning (a false syllogism)” (Arist. Poet. XVI, 1455a 12–13). Secondly, the recognitions described by Aristotle in Part XVI of Poetics occur between two characters, when one has to recognise the other. Therefore, the author of the article does not agree with the opinion by Dana Munteanu (2002) – that in Menander’s comedy Epitrepontes, Smikrine’s false recognition should be referred to as an erroneous spectator’s recognition, whereas at the end of the play Menander depicts Smikrine as a misled spectator just observing the events uninvolved without understanding them properly. By such leaving the word “spectator” in Aristotle’s classification of the fifth type of anagnorisis and using it for a character observing the actions of the play uninvolved, an ambiguity occurs, as Aristotle himself in his Poetics speaks many times about an actual spectator of the tragedy, who while watching the action of the play experiences fear and pity.The author of the article thinks that the translation of chapter XVI of Aristotle’s Poetics by Marcelinas Ročka (1990) should be corrected in some places. At the fifth “recognition by false reasoning”, a note in square brackets stating that this is the last recognition should be omitted. In fact, it is the next-to-last recognition discussed by Aristotle. In translation “the recognitions invented by the poet himself”, some other word can be used, as Aristotle here has in mind that poets usually write poorly and use trite recognitions. A phrase “to contrive” (in Lithuanian “sukurpti”) could be used here instead, as it means “to make or put together roughly or hastily”. It is also true speaking of the translation that a character of the play rather than the audience recognises another character by false reasoning.Finally, the author of the article draws a conclusion that according to Aristotle an anagnorisis is the recognition of persons occurring among characters of the play. In Aristotle’s Poetics, six variants of anagnorisis are distinguished and their classification made based on the principle of artistry and the originality of its use in plays.
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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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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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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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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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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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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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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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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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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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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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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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XU, RUIFENG, DANIEL YEUNG, WENHAO SHU, and JIAFENG LIU. "A HYBRID POST-PROCESSING SYSTEM FOR HANDWRITTEN CHINESE CHARACTER RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 06 (September 2002): 657–79. http://dx.doi.org/10.1142/s0218001402001964.
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In this paper, a hybrid post-processing system for improving the performance of Handwritten Chinese Character Recognition is presented. In order to remove two kinds of frequently encountered errors in the recognition result, namely mis-recognized character and unrecognized character, both confusing character characteristics of the recognizer and the contextual linguistic information are utilized in our hybrid three-stage post-processing system. In the first stage, the confusing character set and a statistical Noisy-Channel model are employed to identify the most promising candidate character and append possible unrecognized similar-shaped characters into candidate character set when a candidate sequence is given. Secondly, dictionary-based approximate word matching is conducted to further append contextual linguistic-prone characters into candidate character set and bind the candidate characters into a word-lattice. Finally, a Chinese word BI-Gram Markov model is employed in the third stage to identify a most promising sentence by selecting plausible words from the word-lattice. On the average, our system achieves a 5.1% recognition rate improvement for the first candidate when the original character recognition rate is 90% for the first candidate and 95% for the top-10 candidates by an online HCCR engine.
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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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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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Wang, Weilan, Zhengjiang Li, Zhengqi Cai, Xiaobao Lv, Caike Zhaxi, and Yuehui Han. "Online Tibetan Handwriting Recognition for Large Character Set on New Databases." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 10 (September 2019): 1953003. http://dx.doi.org/10.1142/s0218001419530033.
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The online handwriting recognition of Tibetan characters is still in its infancy. For further research, an online handwriting database of large Tibetan character set was developed, and a recognition research was carried out on this database as a baseline result. The Northwest Minzu University Online Tibetan Handwriting Database (NMU-OLTHWDB) contains 7240 different types of characters, and the sample number in each type is 5000. The total number of samples is [Formula: see text]. The database covers Tibetan Character Collection, Information Technology Tibetan Coded Character set (Extension Set A), and Information Technology Tibetan Coded Character set (Extension Set B). The characters in the database are composed of 170 types of different components. We studied the online handwritten Tibetan recognition software also, and the character feature extraction, classifier training, and the statistics and analysis of the recognition results on the test set were mainly introduced. The character features included the direction attribute coefficients and spatial combination, and the feature matrix was compressed by Linear Discriminate Analysis (LDA). A quick classifier was designed by a modified quadratic discriminate function (QMQDF), and was trained with 4500 sets of samples. In the large character set, the recognition rates of top 1, top 3, top 5, and top 10 were 75.2%, 89.56%, 93.02%, and 95.96%, respectively. Moreover, an online handwriting recognition system for Tibetan large character set was designed with good performance.
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Dai, Zhenhua, and Jie Yang. "A Multimedia Learning for Chinese Character Image Recognition via Human-Computer Interaction Network." Advances in Multimedia 2022 (May 28, 2022): 1–7. http://dx.doi.org/10.1155/2022/4427091.
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As science and technology continue to develop, Chinese character image recognition technology is being used in a wide range of fields. This computer-based technology is a practical way of automatically recognizing images of text. Typically used in Chinese character education, it provides a new form of human–computer interaction for students. In addition, multimedia technology can provide a rich learning environment for students, which can present information about Chinese characters in the form of pictures, sounds, and videos, thus compensating for the disadvantages of learning Chinese characters by rote in the traditional educational process. The combination of Chinese character image recognition technology and multimedia technology can not only enrich the process of learning Chinese characters, but also promote students’ motivation to learn, thus providing a new and more modern approach to Chinese character education. Based on the study of Chinese character image recognition technology, this research combines it with multimedia information, to achieve the image recognition of Chinese character and multimedia information representation. The combined technology can provide significant references for course design and Chinese learners.
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V. Seeri, Shivananda, J. D. Pujari, and P. S. Hiremath. "PNN Based Character Recognition in Natural Scene Images." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (October 31, 2016): 109–13. http://dx.doi.org/10.9756/bijsesc.8254.
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Pornpanomchai, Chomtip, Verachad Wongsawangtham, Satheanpong Jeungudomporn, and Nannaphat Chatsumpun. "Thai Handwritten Character Recognition by Genetic Algorithm (THCRGA)." International Journal of Engineering and Technology 3, no. 2 (2011): 148–53. http://dx.doi.org/10.7763/ijet.2011.v3.214.
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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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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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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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Zhang, Ce, Weilan Wang, and Guowei Zhang. "Construction of a Character Dataset for Historical Uchen Tibetan Documents under Low-Resource Conditions." Electronics 11, no. 23 (November 27, 2022): 3919. http://dx.doi.org/10.3390/electronics11233919.
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The construction of a character dataset is an important part of the research on document analysis and recognition of historical Tibetan documents. The results of character segmentation research in the previous stage are presented by coloring the characters with different color values. On this basis, the characters are annotated, and the character images corresponding to the annotation are extracted to construct a character dataset. The construction of a character dataset is carried out as follows: (1) text annotation of segmented characters is performed; (2) the character image is extracted from the character block based on the real position information; (3) according to the class of annotated text, the extracted character images are classified to construct a preliminary character dataset; (4) data augmentation is used to solve the imbalance of classes and samples in the preliminary dataset; (5) research on character recognition based on the constructed dataset is performed. The experimental results show that under low-resource conditions, this paper solves the challenges in the construction of a historical Uchen Tibetan document character dataset and constructs a 610-class character dataset. This dataset lays the foundation for the character recognition of historical Tibetan documents and provides a reference for the construction of relevant document datasets.
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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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Liu, Wen Bo, and Tao Wang. "The Character Recognition of Vehicle's License Plate Based on BP Neural Networks." Applied Mechanics and Materials 513-517 (February 2014): 3805–8. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3805.
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This paper based on license plate image preprocessing ,license plate localization, and character segment ,using BP neural network algorithm to identify the license plate characters. Through k-l algorithm of characters on the feature extraction and recognition of license plate character respectively then taking the extraction of license plate character features into the character classifier to the training. When the end of training, extracting the net-work weights and offset matrix, and storing in the computer. To take the identified character images input to the MATLAB, and with the preservation weights and offset matrix operations, obtain the final results of recognition.
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Dey, Raghunath, Rakesh Chandra Balabantaray, and Sanghamitra Mohanty. "Offline Odia handwritten character recognition with a focus on compound characters." Multimedia Tools and Applications 81, no. 8 (February 16, 2022): 10469–95. http://dx.doi.org/10.1007/s11042-022-12148-z.
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Ferreira, C., M. J. Buades, and A. Moya. "Anamorphic correlator for character recognition. Detection of characters of different size." Journal of Optics 20, no. 4 (July 1989): 181–85. http://dx.doi.org/10.1088/0150-536x/20/4/004.
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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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Sangkathum, Ousanee, and Ohm Sornil. "Printed Thai Character Recognition Using Conditional Random Fields and Hierarchical Centroid Distance." Applied Mechanics and Materials 411-414 (September 2013): 1238–46. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1238.
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This paper presents a Thai character recognition method based on topological properties. The method first extracts gradient features from a character image. A two-step classification are then applied to recognize the character. In the first step, a conditional random fields model is used to generate a set of possible characters. Then a nearest neighbor model based on hierarchical centroid distance is employed to finally recognize the character. The proposed method is trained by printed characters from documents and vehicle license plates. The technique is evaluated and found to have the recognition rate of 96.96%.
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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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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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ANIGBOGU, J. C., and A. BELAÏD. "HIDDEN MARKOV MODELS IN TEXT RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 06 (December 1995): 925–58. http://dx.doi.org/10.1142/s0218001495000389.
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A multi-level multifont character recognition is presented. The system proceeds by first delimiting the context of the characters. As a way of enhancing system performance, typographical information is extracted and used for font identification before actual character recognition is performed. This has the advantage of sure character identification as well as text reproduction in its original form. The font identification is based on decision trees where the characters are automatically arranged differently in confusion classes according to the physical characteristics of fonts. The character recognizers are built around the first and second order hidden Markov models (HMM) as well as Euclidean distance measures. The HMMs use the Viterbi and the Extended Viterbi algorithms to which enhancements were made. Also present is a majority-vote system that polls the other systems for “advice” before deciding on the identity of a character. Among other things, this last system is shown to give better results than each of the other systems applied individually. The system finally uses combinations of stochastic and dictionary verification methods for word recognition and error-correction.
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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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Ramesh, Nitin, Aksha Srivastava, and K. Deeba. "Improving Optical Character Recognition Techniques." International Journal of Engineering & Technology 7, no. 2.24 (April 25, 2018): 361. http://dx.doi.org/10.14419/ijet.v7i2.24.12085.
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Document text recognition uses a concept called OCR (optical character recognition),which is the recognition of printed or written text characters by a computer. This involves scanning a document containing text, and converting character by character to their digital form. Thus, it is defined as the process of digitizing a document image into its constituent characters. Equipment used to obtain clearer images for analysis are cameras and flatbed scanners. Even though it’s been out in the world since 1870, the OCR technology is yet to reach perfection. This demanding nature of Optical Character Recognition has made various researchers, industries and technology enthusiasts to divulge their attention to this field. In recent times one can notice a significant increase in the number of research organizations investing their time and effort in this field. In this research, the progress, different aspects and various issues revolving in this field have been summarized. The aim is to present a scrupulous overview of various proposals, advancements and discussions aimed at resolving various problems that arise in traditional OCR.
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Et. al., Rituraj Jain,. "DESIGN OF MACHINE LEARNING MODEL FOR CHARACTER RECOGNITION." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (March 25, 2021): 376–80. http://dx.doi.org/10.17762/itii.v9i2.358.
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Hand written character recognition or text recognition is the ability of the system to identify character or text automatically. Documents can be in any form such as documents, photographs, touch-screens or other devices. Each character will have its own feature sets. The classification or identification of characters are done based on proper selection of feature sets. Feature selection is the major step for any classification process. The machine learning model is created based on feature sets. Logistic regression model is used in this model to identify different characters. 97.33% accuracy is achieved using logistic regression model compared to existing work.
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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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Shobha Rani, N., N. Chandan, A. Sajan Jain, and H. R. Kiran. "Deformed character recognition using convolutional neural networks." International Journal of Engineering & Technology 7, no. 3 (July 26, 2018): 1599. http://dx.doi.org/10.14419/ijet.v7i3.14053.
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Realization of high accuracies towards south Indian character recognition is one the truly interesting research challenge. In this paper, our investigation is focused on recognition of one of the most widely used south Indian script called Kannada. In particular, the proposed exper-iment is subject towards the recognition of degraded character images which are extracted from the ancient Kannada poetry documents and also on the handwritten character images that are collected from various unconstrained environments. The character images in the degraded documents are slightly blurry as a result of which character image is imposed by a kind of broken and messy appearances, this particular aspect leads to various conflicting behaviors of the recognition algorithm which in turn reduces the accuracy of recognition. The training of degraded patterns of character image samples are carried out by using one of the deep convolution neural networks known as Alex net.The performance evaluation of this experimentation is subject towards the handwritten datasets gathered synthetically from users of age groups between 18-21, 22-25 and 26-30 and also printed datasets which are extracted from ancient document images of Kannada poetry/literature. The datasets are comprised of around 497 classes. 428 classes include consonants, vowels, simple compound characters and complex com-pound characters. Each base character combined with consonant/vowel modifiers in handwritten text with overlapping/touching diacritics are assumed as a separate class in Kannada script for our experimentation. However, for those compound characters that are non-overlapping/touching are still considered as individual classes for which the semantic analysis is carried out during the post processing stage of OCR. It is observed that the performance of the Alex net in classification of printed character samples is reported as 91.3% and with reference to handwritten text, and accuracy of 92% is recorded.