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Добірка наукової літератури з теми "Character recogniion"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Character recogniion"

1

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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Анотація:
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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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Дисертації з теми "Character recogniion"

1

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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2

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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3

劉健強 and Kin-keung Lau. "Preprocessing and postprocessing techniques for improving the performance of a Chinese character recognition system." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B31210375.

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4

Wong, Chi-hung, and 黃志雄. "Hand-written Chinese character recognition by hidden Markov models andradical partition." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31220058.

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5

An, Kyung Hee. "Concurrent Pattern Recognition and Optical Character Recognition." Thesis, University of North Texas, 1991. https://digital.library.unt.edu/ark:/67531/metadc332598/.

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The problem of interest as indicated is to develop a general purpose technique that is a combination of the structural approach, and an extension of the Finite Inductive Sequence (FI) technique. FI technology is pre-algebra, and deals with patterns for which an alphabet can be formulated.
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6

AbdelRaouf, Ashraf M. "Offline printed Arabic character recognition." Thesis, University of Nottingham, 2012. http://eprints.nottingham.ac.uk/12601/.

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Optical Character Recognition (OCR) shows great potential for rapid data entry, but has limited success when applied to the Arabic language. Normal OCR problems are compounded by the right-to-left nature of Arabic and because the script is largely connected. This research investigates current approaches to the Arabic character recognition problem and innovates a new approach. The main work involves a Haar-Cascade Classifier (HCC) approach modified for the first time for Arabic character recognition. This technique eliminates the problematic steps in the pre-processing and recognition phases in additional to the character segmentation stage. A classifier was produced for each of the 61 Arabic glyphs that exist after the removal of diacritical marks. These 61 classifiers were trained and tested on an average of about 2,000 images each. A Multi-Modal Arabic Corpus (MMAC) has also been developed to support this work. MMAC makes innovative use of the new concept of connected segments of Arabic words (PAWs) with and without diacritics marks. These new tokens have significance for linguistic as well as OCR research and applications and have been applied here in the post-processing phase. A complete Arabic OCR application has been developed to manipulate the scanned images and extract a list of detected words. It consists of the HCC to extract glyphs, systems for parsing and correcting these glyphs and the MMAC to apply linguistic constrains. The HCC produces a recognition rate for Arabic glyphs of 87%. MMAC is based on 6 million words, is published on the web and has been applied and validated both in research and commercial use.
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7

Cowell, J. R. "Character recognition in unconstrained environments." Thesis, Nottingham Trent University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.277696.

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8

ALVARENGA, EDUARDO PIMENTEL DE. "OPTICAL CHARACTER RECOGNITION FOR AUTOMATED LICENSE PLATE RECOGNITION SYSTEMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=28690@1.

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Анотація:
PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Sistemas de reconhecimento automático de placas (ALPR na sigla em inglês) são geralmente utilizados em aplicações como controle de tráfego, estacionamento, monitoração de faixas exclusivas entre outras aplicações. A estrutura básica de um sistema ALPR pode ser dividida em quatro etapas principais: aquisição da imagem, localização da placa em uma foto ou frame de vídeo; segmentação dos caracteres que compõe a placa; e reconhecimento destes caracteres. Neste trabalho focamos somente na etapa de reconhecimento. Para esta tarefa, utilizamos um Perceptron multiclasse, aprimorado pela técnica de geração de atributos baseada em entropia. Mostramos que é possível atingir resultados comparáveis com o estado da arte, com uma arquitetura leve e que permite aprendizado contínuo mesmo em equipamentos com baixo poder de processamento, tais como dispositivos móveis.
ALPR systems are commonly used in applications such as traffic control, parking ticketing, exclusive lane monitoring and others. The basic structure of an ALPR system can be divided in four major steps: image acquisition, license plate localization in a picture or movie frame; character segmentation; and character recognition. In this work we ll focus solely on the recognition step. For this task, we used a multiclass Perceptron, enhanced by an entropy guided feature generation technique. We ll show that it s possible to achieve results on par with the state of the art solution, with a lightweight architecture that allows continuous learning, even on low processing power machines, such as mobile devices.
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Foullon, Perez Alejandro. "Optical character recognition with the SNT_Grid." Thesis, University of Essex, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.536972.

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10

黃伯光 and Pak-kwong Wong. "Multifont printed Chinese character recognition system." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B31210600.

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Книги з теми "Character recogniion"

1

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

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2

Hirobumi, Nishida, and Yamada Hiromitsu, eds. Optical character recognition. New York: J. Wiley, 1999.

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3

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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4

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

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5

National Bureau of Standards. Character set for handprinting: Category, hardware standard, subcategory, character recognition. Gaithersburg, MD: U.S. Dept. of Commerce, National Bureau of Standards, 1985.

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6

Li, Xiaolin. On-line handwritten Kanji character recognition. Birmingham: University of Birmingham, 1994.

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7

McCarthy, Anne S. Image character recognition (ICR): An introduction. Silver Spring, MD: Association for Information and Image Management, 1994.

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8

H, Ogg Marlene, ed. Optical character recognition: A librarian's guide. Westport, CT: Meckler, 1992.

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9

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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10

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

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Частини книг з теми "Character recogniion"

1

Rajalingam, Mallikka. "Character Recognition." In Text Segmentation and Recognition for Enhanced Image Spam Detection, 71–79. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53047-1_5.

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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

Bennamoun, M., and G. J. Mamic. "Optical Character Recognition." In Object Recognition, 199–220. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-3722-1_5.

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4

Rice, Stephen V., George Nagy, and Thomas A. Nartker. "Introduction." In Optical Character Recognition, 1–6. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5021-1_1.

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5

Rice, Stephen V., George Nagy, and Thomas A. Nartker. "Imaging Defects." In Optical Character Recognition, 7–60. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5021-1_2.

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Rice, Stephen V., George Nagy, and Thomas A. Nartker. "Similar Symbols." In Optical Character Recognition, 61–85. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5021-1_3.

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Rice, Stephen V., George Nagy, and Thomas A. Nartker. "Punctuation." In Optical Character Recognition, 87–111. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5021-1_4.

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Rice, Stephen V., George Nagy, and Thomas A. Nartker. "Typography." In Optical Character Recognition, 113–63. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5021-1_5.

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Rice, Stephen V., George Nagy, and Thomas A. Nartker. "Conclusion." In Optical Character Recognition, 165–69. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5021-1_6.

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10

Setlur, Srirangaraj, and Zhixin Shi. "Asian Character Recognition." In Handbook of Document Image Processing and Recognition, 459–86. London: Springer London, 2014. http://dx.doi.org/10.1007/978-0-85729-859-1_14.

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Тези доповідей конференцій з теми "Character recogniion"

1

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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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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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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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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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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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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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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8

Fang, Shancheng, Hongtao Xie, Jianjun Chen, Jianlong Tan, and Yongdong Zhang. "Learning to Draw Text in Natural Images with Conditional Adversarial Networks." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/101.

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In this work, we propose an entirely learning-based method to automatically synthesize text sequence in natural images leveraging conditional adversarial networks. As vanilla GANs are clumsy to capture structural text patterns, directly employing GANs for text image synthesis typically results in illegible images. Therefore, we design a two-stage architecture to generate repeated characters in images. Firstly, a character generator attempts to synthesize local character appearance independently, so that the legible characters in sequence can be obtained. To achieve style consistency of characters, we propose a novel style loss based on variance-minimization. Secondly, we design a pixel-manipulation word generator constrained by self-regularization, which learns to convert local characters to plausible word image. Experiments on SVHN dataset and ICDAR, IIIT5K datasets demonstrate our method is able to synthesize visually appealing text images. Besides, we also show the high-quality images synthesized by our method can be used to boost the performance of a scene text recognition algorithm.
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Du, Yongkun, Zhineng Chen, Caiyan Jia, Xiaoting Yin, Tianlun Zheng, Chenxia Li, Yuning Du, and Yu-Gang Jiang. "SVTR: Scene Text Recognition with a Single Visual Model." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/124.

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Dominant scene text recognition models commonly contain two building blocks, a visual model for feature extraction and a sequence model for text transcription. This hybrid architecture, although accurate, is complex and less efficient. In this study, we propose a Single Visual model for Scene Text recognition within the patch-wise image tokenization framework, which dispenses with the sequential modeling entirely. The method, termed SVTR, firstly decomposes an image text into small patches named character components. Afterward, hierarchical stages are recurrently carried out by component-level mixing, merging and/or combining. Global and local mixing blocks are devised to perceive the inter-character and intra-character patterns, leading to a multi-grained character component perception. Thus, characters are recognized by a simple linear prediction. Experimental results on both English and Chinese scene text recognition tasks demonstrate the effectiveness of SVTR. SVTR-L (Large) achieves highly competitive accuracy in English and outperforms existing methods by a large margin in Chinese, while running faster. In addition, SVTR-T (Tiny) is an effective and much smaller model, which shows appealing speed at inference. The code is publicly available at https://github.com/PaddlePaddle/PaddleOCR.
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Bratić, Diana, and Nikolina Stanić Loknar. "AI driven OCR: Resolving handwritten fonts recognizability problems." In 10th International Symposium on Graphic Engineering and Design. University of Novi Sad, Faculty of technical sciences, Department of graphic engineering and design,, 2020. http://dx.doi.org/10.24867/grid-2020-p82.

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Optical Character Recognition (OCR) is the electronic or mechanical conversion of images of typed, handwritten, or printed text into machine-encoded text. Advanced systems are capable to produce a high degree of recognition accuracy for most technic fonts, but when it comes to handwritten forms there is a problem occur in recognizing certain characters and limitations with conventional OCR processes persist. It is most pronounced in ascenders (k, b, l, d, h, t) and descenders (g, j, p, q, y). If the characters are linked by ligatures, the ascending and descending strokes are even less recognizable to the scanners. In order to reduce the likelihood of a recognition error, it is a necessary to create a large database of stored characters and their glyphs. Feature extraction decomposes glyphs into features like lines, closed loops, line direction, and line intersections. A Multilayer Perceptron (MLP) neural network based on Back Propagation Neural Network (BPNN) algorithm as a method of Artificial Intelligence (AI) has been used in text identification, classification and recognition using various methods: image pattern based, text-based, mark-based etc. Also, the application of AI generates of a large database of different letter cuts, and modifications, and variation of the same letter character structure. For this purpose, the recognizability test of handwritten fonts was performed. Within main group, subgroups of independent letter characters and letter characters linked by ligatures are created, and reading errors were observed. In each subgroup, four different font families (bold stroke, alternating stroke, monoline stroke, and brush stroke) were tested. In subgroup of independent letter characters, errors were observed in similar rounded lines such as the characters a, and e. In the subgroup of letter characters linked by ligatures, errors were also observed in similar rounded lines such as the letter characters a and e, m and n, but also in ascenders b and l, and descenders g and q. Furthermore, seven letter cuts were made from each basic test letters, and up to are thin, ultra-light, light, regular, semi-bold, bold, and ultra-bold, and stored in the existing EMNIST database. The scanning test was repeated, and recently obtained results showed a decrease in the deviation rate, i.e. higher accuracy. Reducing the number of deviations shows that the neural network gives acceptable answers but requires creation of a larger database within about 56,000 different characters.
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Звіти організацій з теми "Character recogniion"

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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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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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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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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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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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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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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