Готові списки джерел за темами / Recognition of characters

Добірка наукової літератури з теми "Recognition of characters"

Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Recognition of characters"

1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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10

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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Дисертації з теми "Recognition of characters"

1

林依民 and Yi-min Lin. "Computer recognition of printed Chinese characters." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1990. http://hub.hku.hk/bib/B31209919.

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梁祥海 and Cheung-hoi Leung. "Computer recognition of handprinted Chinese characters." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1986. http://hub.hku.hk/bib/B31230660.

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施雷 and Lui Sze. "Computer recognition of printed Chinese characters." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31213601.

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Leung, Cheung-hoi. "Computer recognition of handprinted Chinese characters /." [Hong Kong : University of Hong Kong], 1986. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12322131.

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Lau, Kin-keung. "Preprocessing and postprocessing techniques for improving the performance of a Chinese character recognition system /." [Hong Kong : University of Hong Kong], 1991. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13154345.

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Hu, Marie. "A study of Chinese characters recognition methods." Cincinnati, Ohio : University of Cincinnati, 2002. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=ucin1035813506.

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Wong, Chi-hung. "Hand-written Chinese character recognition by hidden Markov models and radical partition /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19669380.

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Chou, Yu-Ju. "Hemispheric lateralisation in the recognition of Chinese characters." Thesis, University of Edinburgh, 2004. http://hdl.handle.net/1842/24431.

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The central core of this thesis is recognition of Chinese characters, integrated by ten experiments of four main themes. The first theme is hemispheric lateralisation of the length effect in Chinese character recognition. The second theme is to investigate this bilateral effect with Chinese characters presented on both visual fields simultaneously. The outcome confirmed that there was indeed a bilateral advantage found from male Chinese readers. For this group of subjects, the longer the stimuli were, the more bilateral collaboration was found, and the collaboration of bilateral presentation took the least response latency in the lexical decision task than any other unilateral presentation. The third theme of this thesis was to the recognition process, holistic or component, while recognizing Chinese characters. The method was taken from the experiment of Face Illusion (Thompson, 1980, quote from Bruce, V. and Young, A., 1998) in which the eyes of Margaret Thatcher’s portrait were turned upside down and the whole portrait was then inverted again for subjects to recognise. In our own experiment, we replicated the methodology and changed Chinese characters by inverting the phonetic or semantic radicals and then turned the whole character around. The results showed no support for the componential processing hypothesis. The fourth theme of the thesis was a self-collection of errors made by slips of the eyes in reading vertically written Chinese. We presented nine types of reading errors in a chart with examples of each type.
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葉賜權 and Chee-kuen Yip. "Machine recognition of multi-font printed Chinese Characters." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1990. http://hub.hku.hk/bib/B31210120.

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Ren, Manling. "Algorithms for off-line recognition of Chinese characters." Thesis, Nottingham Trent University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.245175.

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Книги з теми "Recognition of characters"

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Suchenwirth, Richard, Jun Guo, Irmfried Hartmann, Georg Hincha, Manfred Krause, and Zheng Zhang. Optical Recognition of Chinese Characters. Wiesbaden: Vieweg+Teubner Verlag, 1989. http://dx.doi.org/10.1007/978-3-663-13999-7.

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Richard, Suchenwirth, ed. Optical recognition of Chinese characters. Braunschweig: Friedr. Vieweg, 1989.

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3

Bushofa, B. M. F. Computer recognition of printed arabic characters. Birmingham: University of Birmingham, 1997.

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4

La voix des personnages. Paris: Les éditions du Cerf, 2011.

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5

Shah, Ashish. Character recognition. Manchester: University of Manchester, Departmentof Computer Science, 1997.

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6

Komori, Saeko. A study of kanji word recognition process for Japanese as a second language. Tokyo: Kazama Shobo, 2009.

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7

engineer), Li Xin (Computer, ed. Ji suan ji bi ji jian bie yu yan zheng de li lun he fang fa: Computer Writer Identification and Verification Theory and Method. Beijing: Qing hua da xue chu ban she, 2012.

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8

Rice, Stephen V., George Nagy, and Thomas A. Nartker. Optical Character Recognition. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5021-1.

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inc, International Resource Development, ed. Optical character recognition. Norwalk, Conn., U.S.A. (6 Prowitt St., Norwalk 06855): International Resource Development, 1985.

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10

Moore, Caroline. Optical character recognition. London: Library & Information Technology Centre and British LibraryResearch & Development Department, 1990.

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Частини книг з теми "Recognition of characters"

1

Watson, Mark. "Recognition of Handwritten Characters." In Common LISP Modules, 71–78. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3186-8_6.

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2

Amin, Adnan, and Sameer Singh. "Optical character recognition: Neural network analysis of hand-printed characters." In Advances in Pattern Recognition, 492–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0033271.

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3

Suen, Ching Y. "Automatic Recognition of Handwritten Characters." In Fundamentals in Handwriting Recognition, 70–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-78646-4_4.

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Borgohain, Olimpia, Pramod Kumar, and Saurabh Sutradhar. "Recognition of Handwritten Assamese Characters." In Algorithms for Intelligent Systems, 223–30. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7041-2_17.

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Suchenwirth, Richard, Jun Guo, Irmfried Hartmann, Georg Hincha, Manfred Krause, and Zheng Zhang. "Chinese Characters: Properties and Problems." In Optical Recognition of Chinese Characters, 3–30. Wiesbaden: Vieweg+Teubner Verlag, 1989. http://dx.doi.org/10.1007/978-3-663-13999-7_2.

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Diesbach, Jonas, Andreas Fischer, Marc Bui, and Anna Scius-Bertrand. "Generating Synthetic Styled Chu Nom Characters." In Frontiers in Handwriting Recognition, 484–97. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21648-0_33.

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Suchenwirth, Richard, Jun Guo, Irmfried Hartmann, Georg Hincha, Manfred Krause, and Zheng Zhang. "Introduction." In Optical Recognition of Chinese Characters, 1–2. Wiesbaden: Vieweg+Teubner Verlag, 1989. http://dx.doi.org/10.1007/978-3-663-13999-7_1.

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Suchenwirth, Richard, Jun Guo, Irmfried Hartmann, Georg Hincha, Manfred Krause, and Zheng Zhang. "Input and Preprocessing: Setting the Stage." In Optical Recognition of Chinese Characters, 31–54. Wiesbaden: Vieweg+Teubner Verlag, 1989. http://dx.doi.org/10.1007/978-3-663-13999-7_3.

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Suchenwirth, Richard, Jun Guo, Irmfried Hartmann, Georg Hincha, Manfred Krause, and Zheng Zhang. "Feature Extraction." In Optical Recognition of Chinese Characters, 55–93. Wiesbaden: Vieweg+Teubner Verlag, 1989. http://dx.doi.org/10.1007/978-3-663-13999-7_4.

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Suchenwirth, Richard, Jun Guo, Irmfried Hartmann, Georg Hincha, Manfred Krause, and Zheng Zhang. "Classification." In Optical Recognition of Chinese Characters, 94–115. Wiesbaden: Vieweg+Teubner Verlag, 1989. http://dx.doi.org/10.1007/978-3-663-13999-7_5.

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Тези доповідей конференцій з теми "Recognition of characters"

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Wasalwar, Yash Prashant, Kishan Singh Bagga, PVRR Bhogendra Rao, and Snehlata Dongre. "Handwritten Character Recognition of Telugu Characters." In 2023 IEEE 8th International Conference for Convergence in Technology (I2CT). IEEE, 2023. http://dx.doi.org/10.1109/i2ct57861.2023.10126377.

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Chen, Jingye, Bin Li, and Xiangyang Xue. "Zero-Shot Chinese Character Recognition with Stroke-Level Decomposition." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/85.

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Анотація:
Chinese character recognition has attracted much research interest due to its wide applications. Although it has been studied for many years, some issues in this field have not been completely resolved yet, \textit{e.g.} the zero-shot problem. Previous character-based and radical-based methods have not fundamentally addressed the zero-shot problem since some characters or radicals in test sets may not appear in training sets under a data-hungry condition. Inspired by the fact that humans can generalize to know how to write characters unseen before if they have learned stroke orders of some characters, we propose a stroke-based method by decomposing each character into a sequence of strokes, which are the most basic units of Chinese characters. However, we observe that there is a one-to-many relationship between stroke sequences and Chinese characters. To tackle this challenge, we employ a matching-based strategy to transform the predicted stroke sequence to a specific character. We evaluate the proposed method on handwritten characters, printed artistic characters, and scene characters. The experimental results validate that the proposed method outperforms existing methods on both character zero-shot and radical zero-shot tasks. Moreover, the proposed method can be easily generalized to other languages whose characters can be decomposed into strokes.
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3

Prameela, N., P. Anjusha, and R. Karthik. "Off-line Telugu handwritten characters recognition using optical character recognition." In 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2017. http://dx.doi.org/10.1109/iceca.2017.8212801.

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Diao, Xiaolei, Daqian Shi, Hao Tang, Qiang Shen, Yanzeng Li, Lei Wu, and Hao Xu. "RZCR: Zero-shot Character Recognition via Radical-based Reasoning." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/73.

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The long-tail effect is a common issue that limits the performance of deep learning models on real-world datasets. Character image datasets are also affected by such unbalanced data distribution due to differences in character usage frequency. Thus, current character recognition methods are limited when applied in the real world, especially for the categories in the tail that lack training samples, e.g., uncommon characters. In this paper, we propose a zero-shot character recognition framework via radical-based reasoning, called RZCR, to improve the recognition performance of few-sample character categories in the tail. Specifically, we exploit radicals, the graphical units of characters, by decomposing and reconstructing characters according to orthography. RZCR consists of a visual semantic fusion-based radical information extractor (RIE) and a knowledge graph character reasoner (KGR). RIE aims to recognize candidate radicals and their possible structural relations from character images in parallel. The results are then fed into KGR to recognize the target character by reasoning with a knowledge graph. We validate our method on multiple datasets, and RZCR shows promising experimental results, especially on few-sample character datasets.
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5

Koerich, A. L., and P. R. Kalva. "Unconstrained handwritten character recognition using metaclasses of characters." In rnational Conference on Image Processing. IEEE, 2005. http://dx.doi.org/10.1109/icip.2005.1530112.

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6

Joe, Kevin George, Meghna Savit, and K. Chandrasekaran. "Offline Character recognition on Segmented Handwritten Kannada Characters." In 2019 Global Conference for Advancement in Technology (GCAT). IEEE, 2019. http://dx.doi.org/10.1109/gcat47503.2019.8978320.

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7

Pacaldo, Joren Mundane, Chi Wee Tan, Wah Pheng Lee, Dustin Gerard Ancog, and Haroun Al Raschid Christopher Macalisang. "Utilizing Synthetically-Generated License Plate Automatic Detection and Recognition of Motor Vehicle Plates in Philippines." In International Conference on Digital Transformation and Applications (ICDXA 2021). Tunku Abdul Rahman University College, 2021. http://dx.doi.org/10.56453/icdxa.2021.1022.

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We investigated the potential use of synthetic data for automatic license plate detection and recognition by detecting and clustering each of the characters on the license plates. We used 36 cascading classifiers (26 letters + 10 numbers) as an individual character to detect synthetically generated license plates. We trained our cascade classifier using a Local Binary Pattern (LBP) as the visual descriptor. After detecting all the characters individually, an investigation has been established in identifying and utilizing a clustering algorithm in grouping these characters for valid license plate recognition. Two clustering algorithms have been considered including Hierarchical and K-means. Investigation results revealed that the hierarchical clustering algorithm approach produces better results in clustering the detecting characters than the K-means. Inaccuracy in the actual detection and recognition of license plates is largely attributed to the false detections in some of the 36 classifiers used in the study. To improve the precision in the detection of plate numbers, it is recommended to have a good classifier for each character detection and utilization of a good clustering algorithm. The proponents concluded that detecting and clustering each character was not an effective approach, however the use of synthetic data in training the classifiers shows promising results. Keywords: Cascading Classifiers, Synthetic Data, Local Binary Pattern, License Plate Recognition
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Hou, Tianyu, Nicoletta Adamo, and Nicholas J. Villani. "Micro-expressions in Animated Agents." In Intelligent Human Systems Integration (IHSI 2022) Integrating People and Intelligent Systems. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001081.

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The purpose of this research was to examine the perception of micro-expressions in animated agents with different visual styles. Specifically, the work reported in the paper sought to examine: (1) whether people can recognize micro-expressions in animated agents, (2) the extent to which the degree of exaggeration of micro-expressions affects recognition, perceived naturalness and intensity of the animated agents’ emotions, and (3) whether there are differences in recognition and perception based on the agent’s visual style (realistic vs stylized). The research work involved two experiments: a recognition study and an emotion rating study; 275 participants participated in each experiment. In the recognition study, the participants watched eight micro-expression animations representing four different emotions. Four animations featured a stylized character and four a realistic character. For each animation, subjects were asked to identify the character’s emotion conveyed by the mi-cro-expression. Results showed that all four emotions for both characters were recognized with an acceptable degree of accuracy. In the emotion rating study, participants watched two sets of eight animation clips. Eight animations in each set featured the characters performing both macro- and micro-expressions, the difference between these two sets was the exaggeration degree of micro-expressions (normal vs exaggerated). Participants were asked to recognize the character’s true emotion (conveyed by the micro-expressions) and rate the naturalness and intensity of the character’s emotion in each clip using a 5-point Likert scale. Findings showed that the degree of exaggeration of the micro-expressions had a significant effect on emotion’s naturalness rating, emotion’s intensity rating, and true emotion recognition, and the character visual style had a significant effect on emotion’s intensity rating.
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Wu, Duan, Peng Gao, Dongying Hu, Ran Xu, Yue Qi, and Yumeng Zhang. "The Relationship Between Simplified Chinese Character Height and Cognition Research in Signage Design." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001608.

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75% of our external information comes from vision, in signage and wayfinding system, characters and graphics have become the most important factor of information cognition. As the main characters in China's signage and wayfinding system, simplified Chinese characters affect the rapid and accurate cognition of information. At present, most of the cognitive research on simplified Chinese characters are learned from the data of Japan and Taiwan. Compared with Latin alphabets, Japanese and Chinese characters are much similar, but there are still significant differences between them. Japanese is a combination of Chinese characters and Kanas, the fonts and the layout of characters are very different. The traditional Chinese characters used in Taiwan are much more complicated than the simplified ones used in mainland China. In order to obtain the data that can really guide the standards of signage design in China, this research carried out a series of experimental studies on simplified Chinese characters’ recognition. Under the condition of fixed font, font weight, color, similar stroke number and character frequency range, the experiment obtained the data of characters’ height and recognition distance by changing characters’ height and recording the corresponding visual recognition reaction time. Then, based on the method of regression analysis, the relationship between the two variables of character height and recognition distance is calculated and visualized. Through indoor simulation and supplementary experiments, the data and conclusions could guide or verify the existing ergonomics data and signage design standard. The research outcome shows the relationship between simplified Chinese character height and cognition distance of on public signage system, which provides a theoretical basis for the related research and design. The results also revealed that with the use of Sans Serif typeface，the minimum of character height in the current design standard can be further increased. This research is still in early stage, in addition to the character height, the influence of stroke number, thickness and background colour contrast of characters still need to be further studied.
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Chaithra, D., and K. Indira. "Handwritten online character recognition for single stroke Kannada characters." In 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, 2017. http://dx.doi.org/10.1109/rteict.2017.8256657.

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Звіти організацій з теми "Recognition of characters"

1

Kumar, Shailesh, Joydeep Ghosh, and Melba Crawford. A Bayesian Pairwise Classifier for Character Recognition. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada396131.

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2

Diniz, C., K. M. Stantz, M. W. Trahan, and J. S. Wagner. Character Recognition Using Genetically Trained Neural Networks. Office of Scientific and Technical Information (OSTI), October 1998. http://dx.doi.org/10.2172/2287.

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3

Garris, M. D., C. L. Wilson, J. L. Blue, G. T. Candela, P. Grother, S. Janet, and R. A. Wilkinson. Massively parallel implementation of character recognition systems. Gaithersburg, MD: National Institute of Standards and Technology, 1992. http://dx.doi.org/10.6028/nist.ir.4750.

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4

Wilkinson, R. Allen, Jon Geist, Stanley Janet, Patrick J. Grother, Christopher J. C. Burges, Robert Creecy, Bob Hammond, et al. The first census optical character recognition system conference. Gaithersburg, MD: National Institute of Standards and Technology, 1992. http://dx.doi.org/10.6028/nist.ir.4912.

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5

Janet, S., P. J. Grother, B. Hammond, N. W. Larsen, R. M. Klear, M. J. Matsko, C. J. C. Burges, et al. The second census optical character recognition systems conference. Gaithersburg, MD: National Institute of Standards and Technology, 1994. http://dx.doi.org/10.6028/nist.ir.5452.

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6

Grother, Patrick J. Karhunen Loeve feature extraction for neural handwritten character recognition. Gaithersburg, MD: National Institute of Standards and Technology, 1992. http://dx.doi.org/10.6028/nist.ir.4824.

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7

Griffiths, M., H. A. J. Russell, and C E Logan. Machine learning applied to geoscience: Geo-referenced character recognition. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2020. http://dx.doi.org/10.4095/321092.

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8

Fuller, J. J., A. Farsaie, and T. Dumoulin. Handwritten Character Recognition Using Feature Extraction and Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, February 1991. http://dx.doi.org/10.21236/ada238294.

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9

Garris, Michael D., and Charles L. Wilson. Reject mechanisms for massively parallel neural network character recognition systems. Gaithersburg, MD: National Institute of Standards and Technology, 1992. http://dx.doi.org/10.6028/nist.ir.4863.

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10

Barnes, C. S. Binary decision clustering for neural network based optical character recognition. Gaithersburg, MD: National Institute of Standards and Technology, 1994. http://dx.doi.org/10.6028/nist.ir.5542.

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