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Journal articles on the topic 'Printed character'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Yao, Yong, Wei Hua Wang, Dong Fang Zhang, and Hong Yan Guo. "Printed Character Database Analysis Based Printed Document Examination." Applied Mechanics and Materials 411-414 (September 2013): 1260–66. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1260.

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This paper presented a study on printed character database image analysis based printed document examination in purpose of identifying the printer which created a suspect printed document. It was composed of printed document image acquisition, image pre-processing, feature extraction and classifier. After characters are extracted and recognized in pre-processing, stroke feature sequence of each text block are calculated, and the HU moments of the sequence are also calculated. Finally, the Euclid distance classifier and MQDF classifier are used to recognize the fonts using the above two kind of

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Tseng, Yi-Hong, Chi-Chang Kuo, and Hsi-Jian Lee. "Typeface Identification for Printed Chinese Characters." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 02 (1998): 173–90. http://dx.doi.org/10.1142/s0218001498000129.

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In this paper, we propose a methodology for identifying typefaces of printed Chinese characters in documents. Three kinds of features, stroke width means, stroke width variations, and aspect ratio, are first used to classify character typefaces as: Black, Li, Kai-Round, or Ming-Song. Each of the last two groups contains two typefaces. Vertical/horizontal stroke width ratios are used to distinguish between the Ming and Song typefaces and accumulative pixel ratio to distinguish between the Kai and Round typefaces. Six different typeface feature distributions measured from 5401 printed Chinese ch

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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 (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 OCR

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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 (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 usin

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NATARAJAN, PREMKUMAR, ZHIDONG LU, RICHARD SCHWARTZ, ISSAM BAZZI, and JOHN MAKHOUL. "MULTILINGUAL MACHINE PRINTED OCR." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 01 (2001): 43–63. http://dx.doi.org/10.1142/s0218001401000745.

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This paper presents a script-independent methodology for optical character recognition (OCR) based on the use of hidden Markov models (HMM). The feature extraction, training and recognition components of the system are all designed to be script independent. The training and recognition components were taken without modification from a continuous speech recognition system; the only component that is specific to OCR is the feature extraction component. To port the system to a new language, all that is needed is text image training data from the new language, along with ground truth which gives t

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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 (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

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Win, Htwe Pa Pa, Phyo Thu Thu Khine, and Khin Nwe Ni Tun. "Character Segmentation Scheme for OCR System." International Journal of Computer Vision and Image Processing 1, no. 4 (2011): 50–58. http://dx.doi.org/10.4018/ijcvip.2011100104.

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Automatic machine-printed Optical Characters or texts Recognizers (OCR) are highly desirable for a multitude of modern IT applications, including Digital Library software. However, the state of the art OCR systems cannot do for Myanmar scripts as the language poses many challenges for document understanding. Therefore, the authors design an Optical Character Recognition System for Myanmar Printed Document (OCRMPD), with several proposed techniques that can automatically recognize Myanmar printed text from document images. In order to get more accurate system, the authors propose the method for

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Yang, Ge, Si Lu Xie, and Jing Huang. "A Character Recognizer Based on BP Network." Applied Mechanics and Materials 738-739 (March 2015): 546–50. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.546.

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A way to recognize printed characters based on BP network was proposed in this paper. It was implemented with C language. After a lot of experiments, the experimental results show that the character recognizer has good validity and correctness. The printed characters can be successfully recognized within the reasonable range of error rate.

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Ouadid, Youssef, Mohamed Fakir, and Brahim Minaoui. "Tifinagh Printed Character Recognition through Structural Feature Extraction." International Journal of Computer Vision and Image Processing 6, no. 2 (2016): 42–53. http://dx.doi.org/10.4018/ijcvip.2016070103.

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In this paper a system for the recognition of printed Tifinagh characters is presented. It is divided into three main steps: preprocessing, feature extraction, and classification. Image quality is enhanced through preprocessing which are: binarization, normalization and thinning. Then the image is given to a proposed structural feature extracting algorithm where the character is divided into several geometrically sample shapes which are segments, then transformed into an undirected graph with unique coordinate of all nodes. The character is classified by matching the graph of the character and

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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 (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), f

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JINDAL, MANISH KUMAR, GURPREET SINGH LEHAL, and RAJENDRA KUMAR SHARMA. "ON SEGMENTATION OF TOUCHING CHARACTERS AND OVERLAPPING LINES IN DEGRADED PRINTED GURMUKHI SCRIPT." International Journal of Image and Graphics 09, no. 03 (2009): 321–53. http://dx.doi.org/10.1142/s0219467809003460.

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Character segmentation plays a very important role in a text recognition system. The simple technique of using inter-character gap for segmentation is useful for fine printed documents, but this technique fails to give satisfactory results if the input text contains touching characters. In this paper, we have proposed two algorithms to segment touching characters, and one algorithm to segment overlapping lines in degraded printed Gurmukhi document. Various categories of touching characters in different zones, along with their solutions, have been proposed. The solution methodology extensively

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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 (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

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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 recogn

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Lu, Yi. "Machine printed character segmentation —; An overview." Pattern Recognition 28, no. 1 (1995): 67–80. http://dx.doi.org/10.1016/0031-3203(94)00068-w.

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Hassin, Abbas H., Xiang-Long Tang, Jia-Feng Liu, and Wei Zhao. "Printed Arabic character recognition using HMM." Journal of Computer Science and Technology 19, no. 4 (2004): 538–43. http://dx.doi.org/10.1007/bf02944755.

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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 (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 diction

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Converso, L., and S. Hocek. "Optical Character Recognition." Journal of Visual Impairment & Blindness 84, no. 10 (1990): 507–9. http://dx.doi.org/10.1177/0145482x9008401004.

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Computer-based optical character recognition (OCR) systems allow blind persons access to a wide variety of printed material. This article describes these systems and how they work and discusses the features that should be considered before one purchases them.

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LIN, ZHEN YONG, and PING LIU. "STRUCTURAL ATTRIBUTE FEATURE CODE REPRESENTATION AND RECOGNITION OF MULTIFONT PRINTED CHINESE CHARACTERS." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 02 (2001): 287–309. http://dx.doi.org/10.1142/s0218001401000885.

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In this paper, a new structural representation and fuzzy matching scheme are proposed for multifont printed Chinese character recognition. A Chinese character is decomposed into eight stroke types. A complete structural attribute feature codes among different types of strokes are defined and extracted, which consist of weak and strong primary codes and secondary codes. Weak and strong primary feature codes depict the global and local spatial relationships among different types of strokes respectively, and they are used for a detailed match. A fuzzy matching scheme is used for detailed match be

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Ham, Dae-Sung, Duk-Ryong Lee, In-Suk Jung, and Il-Seok Oh. "Construction of Printed Hangul Character Database PHD08." Journal of the Korea Contents Association 8, no. 11 (2008): 33–40. http://dx.doi.org/10.5392/jkca.2008.8.11.033.

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Muchallil, Sayed, and Nazaruddin. "Online Application of Printed Jawi Character Recognition." Indian Journal of Science and Technology 10, no. 12 (2017): 1–5. http://dx.doi.org/10.17485/ijst/2017/v10i12/113012.

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De Luca, P. G., and A. Gisotti. "Printed character preclassification based on word structure." Pattern Recognition 24, no. 7 (1991): 609–15. http://dx.doi.org/10.1016/0031-3203(91)90028-4.

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Puri, Shalini, and Satya Prakash Singh. "A Hybrid Hindi Printed Document Classification System Using SVM and Fuzzy." Journal of Information Technology Research 12, no. 4 (2019): 107–31. http://dx.doi.org/10.4018/jitr.2019100106.

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This article introduces a new advanced tri-layered segmentation and bi-leveled-classifier-based Hindi printed document classification system, which categorizes imaged documents into pre-defined mutually exclusive categories by using SVM and Fuzzy matching at character and document classifications, respectively. During training, the improved and noise-free image is segmented into lines and words by profiling. Then it obtains Shirorekha Less (SL) isolated characters along with upper, left and right modifier components from the SL words. These components use their locations and inter character-mo

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Kaur, Manpreet, and Balwinder Singh. "Classification of printed and handwritten text using hybrid techniques for gurumukhi script." International Journal of Engineering and Computer Science 8, no. 04 (2019): 24586–602. http://dx.doi.org/10.18535/ijecs/v8i04.4298.

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Text classification is a crucial step for optical character recognition. The output of the scanner is non- editable. Though one cannot make any change in scanned text image, if required. Thus, this provides the feed for the theory of optical character recognition. Optical Character Recognition (OCR) is the process of converting scanned images of machine printed or handwritten text into a computer readable format. The process of OCR involves several steps including pre-processing after image acquisition, segmentation, feature extraction, and classification. The incorrect classification is like

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Anugrah, Rio, and Ketut Bayu Yogha Bintoro. "Latin Letters Recognition Using Optical Character Recognition to Convert Printed Media Into Digital Format." Jurnal Elektronika dan Telekomunikasi 17, no. 2 (2017): 56. http://dx.doi.org/10.14203/jet.v17.56-62.

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Printed media is still popular now days society. Unfortunately, such media encountered several drawbacks. For example, this type of media consumes large storage that impact in high maintenance cost. To keep printed information more efficient and long-lasting, people usually convert it into digital format. In this paper, we built Optical Character Recognition (OCR) system to enable automatic conversion the image containing the sentence in Latin characters into digital text-shaped information. This system consists of several interrelated stages including preprocessing, segmentation, feature extr

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Mukti, Mousumi Hasan, Quazi Saad-Ul-Mosaher, and Khalil Ahammad. "Bengali Longhand Character Recognition using Fourier Transform and Euclidean Distance Metric." European Journal of Engineering Research and Science 3, no. 7 (2018): 67. http://dx.doi.org/10.24018/ejers.2018.3.7.831.

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Handwritten Character Recognition (HCR) is widely considered as a benchmark problem for pattern recognition and artificial intelligence. Text matching has become a popular research area in recent days as it plays a great part in pattern recognition. Different techniques for recognizing handwritten letters and digits for different languages have already been implemented throughout the world. This research aims at developing a system for recognizing Bengali handwritten characters i.e. letters and digits using Fourier Transform (FT) and Euclidean distance measurement technique. A dataset with 800

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BOUSLAMA, FAOUZI. "NEURAL NETWORKS IN THE RECOGNITION OF MACHINE PRINTED ARABIC CHARACTERS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 03 (1999): 395–414. http://dx.doi.org/10.1142/s0218001499000239.

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The objective of this study is to analyze and compare three different recognition approaches to machine printed Arabic characters. The first approach is a template matching and a correlation technique where an input character is compared to a standard set of stored prototype images. The second and the third approaches are based on feature analysis and matching. The features in the second approach are extracted from the horizontal and vertical projections of the images of characters. The third approach is a structural approach where the features are extracted from the geometry of the segments t

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Yuen, P. C., G. C. Feng, and Y. Y. Tang. "Printed Chinese Character Similarity Measurement Using Ring Projection and Distance Transform." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 02 (1998): 209–21. http://dx.doi.org/10.1142/s0218001498000142.

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This paper presents a new Chinese character similarity measurement method based on the ring projection algorithm and distance transform. The ring projection algorithm is used to transform a character image with two independent variables into a function of one independent variable in the ring projection space. This representation of character in the ring projection space has been proved to be in orientation and scale invariant. However, this representation will be distorted nonlinearly in the presence of noise. Therefore, common linear metrics such as Euclidean distance, cannot be applied to me

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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 (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

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Yang, Tai-Ning, and Sheng-De Wang. "A rotation invariant printed Chinese character recognition system." Pattern Recognition Letters 22, no. 2 (2001): 85–95. http://dx.doi.org/10.1016/s0167-8655(00)00089-1.

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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 (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 o

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CHANG, TIEN-CHIEN, and SHU-YUAN CHEN. "CHARACTER SEGMENTATION USING CONVEX-HULL TECHNIQUES." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 06 (1999): 833–58. http://dx.doi.org/10.1142/s021800149900046x.

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A novel character segmentation method for printed documents is proposed in this paper. It is very difficult to process touching, overlapping and broken characters simultaneously. The strategy of our method is to adjust the binarization parameters such that broken characters can be avoided. On the contrary, adjacent characters may spread into each other seriously. Henceforth, the character segmentation problem can be focused on touching-character detection and separation. In the proposed approach, touching characters can be detected using the topological attributes of characters and the typogra

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Taha, Mohamed, Noha Abd-ElKareem, and Mazen Selim. "Real-Time Arabic Text-Reading for Visually Impaired People." International Journal of Sociotechnology and Knowledge Development 13, no. 2 (2021): 168–85. http://dx.doi.org/10.4018/ijskd.2021040110.

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Visually impaired (VI) people suffer from many difficulties when accessing printed material using existing technologies. These problems may include text alignment, focus, accuracy, software processing speed, mobility, and efficiency. Current technologies such as flatbed scanners and OCR programs need to scan an entire page. Recently, VI people prefer mobile devices because of their handiness and accessibility, but they have problems with focusing the mobile camera on the printed material. In this paper, a real-time Arabic text-reading prototype for VI people is proposed. It is based on using a

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Zaqout, Ihab. "Printed Arabic Characters Classification using A Statistical Approach." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 1 (2012): 1–5. http://dx.doi.org/10.24297/ijct.v3i1a.2719.

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In this paper, we propose simple classifiers for printed Arabic characters based on statistical analysis. 109 printed Arabic character images are created for each one of transparent, simplified and traditional Arabic fonts. Images are preprocessed by the binarization and followed by sequence of morphological operations. A non-linear filter is applied on the thinned ridge map to extract termination and bifurcation features. The thinned ridge map vectors (TRMVs) are created using a freeman chain code template. The spatial distribution and statistical properties of the extracted features are calc

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Reul, Christian, Christoph Wick, Uwe Springmann, and Frank Puppe. "Transfer Learning for OCRopus Model Training on Early Printed Books." 027.7 Zeitschrift für Bibliothekskultur 5, no. 1 (2017): 38–51. http://dx.doi.org/10.12685/027.7-5-1-169.

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A method is presented that significantly reduces the character error rates for OCR text obtained from OCRopus models trained on early printed books when only small amounts of diplomatic transcriptions are available. This is achieved by building from already existing models during training instead of starting from scratch. To overcome the discrepancies between the set of characters of the pretrained model and the additional ground truth the OCRopus code is adapted to allow for alphabet expansion or reduction. The character set is now capable of flexibly adding and deleting characters from the p

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Syahrial, Syahrial, Asrial Asrial, Dwi Agus Kurniawan, and Latipia Damayanti. "Comparison of Print Modules and E-Modules to the Tolerance Character of Students." International Journal of Elementary Education 5, no. 2 (2021): 298. http://dx.doi.org/10.23887/ijee.v5i2.34351.

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Nowadays, the character of tolerance in elementary school students is experiencing degradation. Whereas the character of tolerance is one of the important characters possessed by students in their social life. This study aims to analyze the comparison of responses and tolerance characteristics of students who learn to use two different learning modules, namely the electronic module and the printed module. This research is quantitative descriptive. The population in this study amounted to 243 students. Determination of the sample is done by using the purposive sampling technique, to obtain a sa

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Xian, Xiao Ping. "Machine-Printed Invoice Number Based on Fuzzy Recognition." Applied Mechanics and Materials 214 (November 2012): 705–10. http://dx.doi.org/10.4028/www.scientific.net/amm.214.705.

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A new fuzzy recognition method of machine-printed invoice number based on neural network is presented. This method includes ten links: invoice number detection and separation of right on top of invoice, binarization, denoising, incline correction, extraction of invoice code numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. Through testing, the recognition rate of this method can be over 99%.The recognition time of characters for character is less than 1 second, which means that the method is of more eff

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AMIN, ADNAN. "RECOGNITION OF HAND-PRINTED LATIN CHARACTERS BASED ON GENERALIZED HOUGH TRANSFORM AND DECISION TREE LEARNING TECHNIQUES." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 03 (2000): 369–87. http://dx.doi.org/10.1142/s0218001400000246.

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This paper presents a new technique for the recognition of hand-printed Latin characters using machine learning. Conventional methods have relied on manually constructed dictionaries which are not only tedious to construct but also difficult to make tolerant to variation in writing styles. The advantages of machine learning are that it can generalize over a large degree of variation between writing styles, and recognition rules can be constructed by example. Characters are scanned into the computer and preprocessing techniques transform the bit-map representation of the characters into a set o

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YOU, XINGE, QIUHUI CHEN, BIN FANG, and YUAN YAN TANG. "THINNING CHARACTER USING MODULUS MINIMA OF WAVELET TRANSFORM." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 03 (2006): 361–75. http://dx.doi.org/10.1142/s0218001406004764.

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An essential step in character recognition is to extract the skeleton characteristics of the character. In this paper, an efficient algorithm is proposed to extract visually satisfactory skeleton from printed and handwritten characters, which overcomes fundamental shortcomings of our previous skeletonization technique based on the maximum modulus symmetry of wavelet transform (WT). The proposed method is motivated from some desirable properties of the WT with constructed wavelet functions: namely, the local modulus minima of the WT are scale-independent at different level scales and are locate

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F.M. Mohammad, Hanaa. "Recognition of Printed Arabic Character Using Gray- Scale Matrices." JOURNAL OF EDUCATION AND SCIENCE 18, no. 4 (2006): 157–67. http://dx.doi.org/10.33899/edusj.2006.77663.

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Seethalakshmi, R., T. R. Sreeranjani, T. Balachandar, et al. "Optical character recognition for printed Tamil text using Unicode." Journal of Zhejiang University-SCIENCE A 6, no. 11 (2005): 1297–305. http://dx.doi.org/10.1631/jzus.2005.a1297.

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Al- Obaidi, Mahdi, and Laheeb Ibrahim. "Software for Arabic Machine Printed Optical Character Recognition (MACRS)." AL-Rafidain Journal of Computer Sciences and Mathematics 3, no. 1 (2006): 23–41. http://dx.doi.org/10.33899/csmj.2006.164033.

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Zheng, Liying, Abbas H. Hassin, and Xianglong Tang. "A new algorithm for machine printed Arabic character segmentation." Pattern Recognition Letters 25, no. 15 (2004): 1723–29. http://dx.doi.org/10.1016/j.patrec.2004.06.015.

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Aghav, Sushila, and S. S. Paygude. "Computer Assisted Printed Character Recognition in Document Based Images." Procedia Engineering 38 (2012): 3222–27. http://dx.doi.org/10.1016/j.proeng.2012.06.373.

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OYA, Makoto, and Yosuke SATAKE. "617 Defect Inspection of Printed Character by Image Processing." Proceedings of Conference of Hokuriku-Shinetsu Branch 2005.42 (2005): 211–12. http://dx.doi.org/10.1299/jsmehs.2005.42.211.

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OYA, Makoto, and Yuya SATO. "620 Defect Inspection of Printed Character by Image Processing." Proceedings of Conference of Hokuriku-Shinetsu Branch 2006.43 (2006): 207–8. http://dx.doi.org/10.1299/jsmehs.2006.43.207.

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Namane, A., A. Guessoum, E. H. Soubari, and P. Meyrueis. "CSM neural network for degraded printed character optical recognition." Journal of Visual Communication and Image Representation 25, no. 5 (2014): 1171–86. http://dx.doi.org/10.1016/j.jvcir.2014.04.002.

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Fang, Chi, Changsong Liu, Liangrui Peng, and Xiaoqing Ding. "Automatic performance evaluation of printed Chinese character recognition systems." International Journal on Document Analysis and Recognition 4, no. 3 (2002): 177–82. http://dx.doi.org/10.1007/s100320200068.

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Kaznin, A. "Recognition Handwriting and Printed Text for Software Requirements Engineering." Bulletin of Science and Practice 5, no. 12 (2019): 246–56. http://dx.doi.org/10.33619/2414-2948/49/29.

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This article discusses the problems of collecting software requirements. The existing computer vision technologies are analyzed and the choice of technology for recognizing handwritten and printed text is justified. The input data for the experiments are described and the results of character recognition for each image category are presented. A method of image preprocessing and recognition of text characters on mobile devices using parallel computing has been developed. On the basis of the proposed method, a prototype mobile application for collecting and digitalizing data obtained during the

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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 (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

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Dena Rafaa, Ahmed, and Jan Nordin. "Offline OCR System for Machine-Printed Turkish Using Template Matching." Advanced Materials Research 341-342 (September 2011): 565–69. http://dx.doi.org/10.4028/www.scientific.net/amr.341-342.565.

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One of the most important application these days in Pattern Recognition (PR) is Optical Character recognition (OCR) which is a system used to convert scanned printed or handwritten image files into machine readable and editable format such as text documents. The main motivation behind this study is to build an OCR system for offline machine-printed Turkish characters to convert any image file into a readable and editable format. This OCR system started from preprocessing step to convert the image file into a binary format with less noise to be ready for recognition. The preprocessing step incl

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