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Relevant bibliographies by topics / Optical character recognition; Writing

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Academic literature on the topic 'Optical character recognition; Writing'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Optical character recognition; Writing"

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Yaseen, Rasty, and Hossein Hassani. "Kurdish Optical Character Recognition." UKH Journal of Science and Engineering 2, no. 1 (2018): 18–27. http://dx.doi.org/10.25079/ukhjse.v2n1y2018.pp18-27.

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Currently, no offline tool is available for Optical Character Recognition (OCR) in Kurdish. Kurdish is spoken in different dialects and uses several scripts for writing. The Persian/Arabic script is widely used among these dialects. The Persian/Arabic script is written from Right to Left (RTL), it is cursive, and it uses unique diacritics. These features, particularly the last two, affect the segmentation stage in developing a Kurdish OCR. In this article, we introduce an enhanced character segmentation based method which addresses the mentioned characteristics. We applied the method to text-o

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Supriadi, Supriadi. "Simple Handwriting Calculator Application Using Optical Character Recognition (OCR)." Buletin Ilmiah Sarjana Teknik Elektro 2, no. 3 (2021): 163. http://dx.doi.org/10.12928/biste.v2i3.3348.

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The calculator is a calculation tool that is widely used in various specialized fields of business and commerce. The use of a calculator makes it easier for humans to perform calculation operations, but there are obstacles in the process of inputting numbers if you want to calculate the value of numbers on written media such as paper, whiteboards and so on. The user must first see the text on written media, then read it and remember it then type the writing on a calculator tool or application. The drawback of this method is that when the user forgets the writing on the written media, the user

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Supriadi, Supriadi. "Simple Handwriting Calculator Application Using Optical Character Recognition (OCR)." Buletin Ilmiah Sarjana Teknik Elektro 2, no. 3 (2021): 163. http://dx.doi.org/10.12928/biste.v2i3.3348.

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The calculator is a calculation tool that is widely used in various specialized fields of business and commerce. The use of a calculator makes it easier for humans to perform calculation operations, but there are obstacles in the process of inputting numbers if you want to calculate the value of numbers on written media such as paper, whiteboards and so on. The user must first see the text on written media, then read it and remember it then type the writing on a calculator tool or application. The drawback of this method is that when the user forgets the writing on the written media, the user

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Supriadi, Supriadi. "Aplikasi Kalkulator Tulisan Tangan Sederhana Menggunakan Optical Character Recognition (OCR)." Applied Technology and Computing Science Journal 3, no. 2 (2021): 103–16. http://dx.doi.org/10.33086/atcsj.v3i2.1867.

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The calculator is a calculation tool that is widely used in various specialized fields of business and commerce. The use of a calculator makes it easier for humans to perform arithmetic operations, but there are obstacles in the process of inputting numbers if you want to calculate the value of numbers on written media such as paper, whiteboards and so on. The user must first see the text on written media, then read it and remember it then type the writing on a calculator tool or application. The drawback of this method is that when the user forgets the writing on the written media, the user w

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Pino, Rodney, Renier Mendoza, and Rachelle Sambayan. "Optical character recognition system for Baybayin scripts using support vector machine." PeerJ Computer Science 7 (February 15, 2021): e360. http://dx.doi.org/10.7717/peerj-cs.360.

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In 2018, the Philippine Congress signed House Bill 1022 declaring the Baybayin script as the Philippines’ national writing system. In this regard, it is highly probable that the Baybayin and Latin scripts would appear in a single document. In this work, we propose a system that discriminates the characters of both scripts. The proposed system considers the normalization of an individual character to identify if it belongs to Baybayin or Latin script and further classify them as to what unit they represent. This gives us four classification problems, namely: (1) Baybayin and Latin script recogn

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Liu, Zhiji. "Optical character recognition and the smart ancient script database." Journal of Chinese Writing Systems 4, no. 4 (2020): 255–69. http://dx.doi.org/10.1177/2513850220967758.

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The initial success of optical character recognition (OCR) for ancient scripts has opened the floodgates for ‘smart’ ancient script research. ‘Smart’ ancient script research requires the support of a smart ancient script database. In order to compile the big data necessary for this smart research, smart ancient script database software must be able to recognize all aspects and all levels of all ancient script materials. Therefore, in addition to the integration of OCR functionality into this software, the other primary imperative moving forward is to innovate a new digitized ancient script dat

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Susanty, Meredita, and Herminarto Nugroho. "OPTICAL CHARACTER RECOGNITION IMPLEMENTATION FOR ADMISSION SYSTEM IN UNIVERSITAS PERTAMINA." Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer 11, no. 1 (2020): 165–70. http://dx.doi.org/10.24176/simet.v11i1.3838.

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Starting in 2019, prospective college students require to take Computer-Based Writing Exam (UTBK) to register for the state universities in Indonesia. Some private university also adopts this exam as a requirement for admission. One of the private university that adopts it is Universitas Pertamina. UTBK consist of several exam group score printed in a digital certificate in image format (jpg). The university admission team must download the UTBK certificate that has uploaded by applicants, read and record the score for each exam group then make a calculation to make a decision whether the appl

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Alam, Md Shahinur, Ki-Chul Kwon, Md Ashraful Alam, Mohammed Y. Abbass, Shariar Md Imtiaz, and Nam Kim. "Trajectory-Based Air-Writing Recognition Using Deep Neural Network and Depth Sensor." Sensors 20, no. 2 (2020): 376. http://dx.doi.org/10.3390/s20020376.

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Trajectory-based writing system refers to writing a linguistic character or word in free space by moving a finger, marker, or handheld device. It is widely applicable where traditional pen-up and pen-down writing systems are troublesome. Due to the simple writing style, it has a great advantage over the gesture-based system. However, it is a challenging task because of the non-uniform characters and different writing styles. In this research, we developed an air-writing recognition system using three-dimensional (3D) trajectories collected by a depth camera that tracks the fingertip. For bette

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Kawakami, Yoshihisa, Yoshiteru Akezaki, Reiko Aramaki, and Takuo Nomura. "Comparison between the subjective evaluation and the Optical Character Recognition evaluation in the writing accuracy evaluation." Journal of allied health sciences 5, no. 1 (2014): 35–39. http://dx.doi.org/10.15563/jalliedhealthsci.5.35.

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Zeki, Ahmed M., Mohamad S. Zakaria, and Choong-Yeun Liong. "Segmentation of Arabic Characters." International Journal of Technology Diffusion 2, no. 4 (2011): 48–82. http://dx.doi.org/10.4018/jtd.2011100104.

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The cursive nature of Arabic writing is the main challenge to Arabic Optical Character Recognition developer. Methods to segment Arabic words into characters have been proposed. This paper provides a comprehensive review of the methods proposed by researchers to segment Arabic characters. The segmentation methods are categorized into nine different methods based on techniques used. The advantages and drawbacks of each are presented and discussed. Most researchers did not report the segmentation accuracy in their research; instead, they reported the overall recognition rate which did not reflec

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Dissertations / Theses on the topic "Optical character recognition; Writing"

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Monger, David M. "The human factors aspects of interactive document image description for OCR of handwritten forms." Thesis, University of Essex, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.238747.

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Chen, Wen-Tsong. "Word level training of handwritten word recognition systems /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9974612.

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Lau, Kai Kwong Gervas. "A new statistical stroke recovery method and measurement for signature verification." HKBU Institutional Repository, 2005. http://repository.hkbu.edu.hk/etd_ra/661.

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Pang, Bo. "Handwriting Chinese character recognition based on quantum particle swarm optimization support vector machine." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950620.

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Ansari, Nasser. "Handwritten character recognition by using neural network based methods." Ohio : Ohio University, 1992. http://www.ohiolink.edu/etd/view.cgi?ohiou1172080742.

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Chai, Sin-Kuo. "Multiclassifier neural networks for handwritten character recognition." Ohio : Ohio University, 1995. http://www.ohiolink.edu/etd/view.cgi?ohiou1174331633.

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Bristow, Kelly H. "Freeform Cursive Handwriting Recognition Using a Clustered Neural Network." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc804845/.

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Optical character recognition (OCR) software has advanced greatly in recent years. Machine-printed text can be scanned and converted to searchable text with word accuracy rates around 98%. Reasonably neat hand-printed text can be recognized with about 85% word accuracy. However, cursive handwriting still remains a challenge, with state-of-the-art performance still around 75%. Algorithms based on hidden Markov models have been only moderately successful, while recurrent neural networks have delivered the best results to date. This thesis explored the feasibility of using a special type of feedf

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Coetzer, Johannes. "Off-line signature verification." Thesis, Stellenbosch : University of Stellenbosch, 2005. http://hdl.handle.net/10019.1/1355.

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Thesis (PhD (Mathematical Sciences))--University of Stellenbosch, 2005.<br>A great deal of work has been done in the area of off-line signature verification over the past two decades. Off-line systems are of interest in scenarios where only hard copies of signatures are available, especially where a large number of documents need to be authenticated. This dissertation is inspired by, amongst other things, the potential financial benefits that the automatic clearing of cheques will have for the banking industry.

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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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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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Books on the topic "Optical character recognition; Writing"

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Liu, Zhi-Qiang. Handwriting recognition: Soft computing and probabilistic approaches. Springer, 2003.

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Gorskiĭ, N. D. Razpoznavanie rukopisnogo teksta: Ot teorii k praktike. Politekhnika, 1997.

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name, No. Document recognition and retrieval X: 22-24 January 2003, Santa Clara, California, USA. SPIE, 2002.

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Schenkel, Markus E. Handwriting recognition using neural networks and hidden Markov models. Hartung-Gorre, 1995.

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Netherlands) International Workshop on Frontiers in Handwriting Recognition (7th 2000 Amsterdam. 7th International Workshop on Frontiers in Handwriting Recognition: September 11-13, 2000, Amsterdam, the Netherlands : proceedings. International Unipen Foundation, 2000.

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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. Qing hua da xue chu ban she, 2012.

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Colloque international francophone sur l'črit et le document (1st 1998 Qub̌ec, Quebec). Actes du 1er Colloque international francophone sur l'écrit et le document: Tenu dans le cadre du Congrès de l'Acfas à Québec en mai 1998. Association canadienne-française pour l'avancement des sciences, 1998.

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S, Doermann David, and Jäger Stefan Dr, eds. Arabic and Chinese handwriting recognition: SACH 2006 summit, College Park, MD, USA, September 27-28, 2006 : selected papers. Springer, 2008.

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Liu, Z. Q. Handwriting recognition: Soft computing and probabilistic approaches. Springer-Verlag, 2003.

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Workshop on Document Image Analysis (1997 San Juan, P.R.). Proceedings, Workshop on Document Image Analysis (DIA '97): June 20, 1997, San Juan, Puerto Rico : in cooperation with CVPR '97. IEEE Computer Society Press, 1997.

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Book chapters on the topic "Optical character recognition; Writing"

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

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Weik, Martin H. "optical character recognition." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_12944.

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Nahler, Gerhard. "optical character recognition." In Dictionary of Pharmaceutical Medicine. Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_973.

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

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

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Gooch, Jan W. "Optical Character Recognition Inks." In Encyclopedic Dictionary of Polymers. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_8212.

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Conference papers on the topic "Optical character recognition; Writing"

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Duth P., Sudharshan, and B. Amulya. "Recognition of Hand written and Printed Text of Cursive Writing Utilizing Optical Character Recognition." In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2020. http://dx.doi.org/10.1109/iciccs48265.2020.9121080.

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Sano, Tetsuya, Hiroyuki Ukida, and Hideki Yamamoto. "Recognition of Hand Writing Japanese Character." In 2007 4th IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications. IEEE, 2007. http://dx.doi.org/10.1109/idaacs.2007.4488447.

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Syamlan, Adlina Taufik, Hendro Nurhadi, and Bambang Pramujati. "Character recognition for Writing Robot Control using ANFIS." In 2015 International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA). IEEE, 2015. http://dx.doi.org/10.1109/icamimia.2015.7508000.

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Sattar, Sohail A., Shamsul Haque, and Mahmood K. Pathan. "Nastaliq optical character recognition." In the 46th Annual Southeast Regional Conference. ACM Press, 2008. http://dx.doi.org/10.1145/1593105.1593192.

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Lianwen Jin, Duanduan Yang, Li-Xin Zhen, and Jian-Cheng Huang. "A Novel Vision based Finger-writing Character Recognition System." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.145.

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Sagar, B. M., G. Shobha, and P. Ramakanth Kumar. "Character segmentation algorithms for Kannada optical character recognition." In 2008 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2008. http://dx.doi.org/10.1109/icwapr.2008.4635800.

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Anand, Vipin, and Ankit Navik. "Optical Character Recognition based Zooming." In 2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE). IEEE, 2018. http://dx.doi.org/10.1109/icrieece44171.2018.9009356.

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Garris, Michael D., James L. Blue, Gerald T. Candela, et al. "Public domain optical character recognition." In IS&T/SPIE's Symposium on Electronic Imaging: Science & Technology, edited by Luc M. Vincent and Henry S. Baird. SPIE, 1995. http://dx.doi.org/10.1117/12.205810.

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Tuncer, Esra, Bilal Orkan Olcay, and Mehmet Zubeyir Unlu. "Handwritten character recognition using Empirical Mode Decomposition applied writing movements." In 2017 25th Signal Processing and Communications Applications Conference (SIU). IEEE, 2017. http://dx.doi.org/10.1109/siu.2017.7960527.

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Alam, Md Shahinur, Ki-Chul Kwon, and Nam Kim. "Trajectory-Based Air-Writing Character Recognition Using Convolutional Neural Network." In 2019 4th International Conference on Control, Robotics and Cybernetics (CRC). IEEE, 2019. http://dx.doi.org/10.1109/crc.2019.00026.

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Reports on the topic "Optical character recognition; Writing"

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Janet, S., P. J. Grother, B. Hammond, et al. The second census optical character recognition systems conference. National Institute of Standards and Technology, 1994. http://dx.doi.org/10.6028/nist.ir.5452.

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Wilkinson, R. Allen, Jon Geist, Stanley Janet, et al. The first census optical character recognition system conference. National Institute of Standards and Technology, 1992. http://dx.doi.org/10.6028/nist.ir.4912.

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Garris, Michael D., Stanley Janet, and William W. Klein. Impact of image quality on machine print optical character recognition. National Institute of Standards and Technology, 1997. http://dx.doi.org/10.6028/nist.ir.6101.

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

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Kjersten, Brian. Arabic Optical Character Recognition (OCR) Evaluation in Order to Develop a Post-OCR Module. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada554465.

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Davidson, Robert B., and Richard L. Hopely. Foreign Language Optical Character Recognition, Phase II: Arabic and Persian Training and Test Data Sets. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada325444.

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Evaluating form designs for optical character recognition. National Institute of Standards and Technology, 1994. http://dx.doi.org/10.6028/nist.ir.5364.

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Federal Information Processing Standards Publication: optical character recognition (OCR) - DOT matrix character sets for OCR-MA. National Bureau of Standards, 1987. http://dx.doi.org/10.6028/nbs.fips.129-1987.

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Federal Information Processing Standards Publication: for information systems - optical character recognition (OCR) - matrix character sets for OCR-MA. National Bureau of Standards, 1986. http://dx.doi.org/10.6028/nbs.fips.129.

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