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Relevant bibliographies by topics / Optical character recognition; Writing / Dissertations / Theses

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

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Published: 4 June 2021

Last updated: 1 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Duewer, Trent A. "Research in Japanese optical character recognition." Full text, Acrobat Reader required, 1998. http://viva.lib.virginia.edu/etd/these/duewer98.pdf.

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12

Santos, Claudio Filipi Gonçalves dos. "Optical character recognition using deep learning." Universidade Estadual Paulista (UNESP), 2018. http://hdl.handle.net/11449/154100.

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Submitted by Claudio Filipi Gonçalves dos Santos (cfsantos85@gmail.com) on 2018-05-24T11:51:59Z No. of bitstreams: 1 optical-character-recognition-16052018.pdf: 8334356 bytes, checksum: 8dd05363a96c946ae1f6d665edc80d09 (MD5)<br>Rejected by Elza Mitiko Sato null (elzasato@ibilce.unesp.br), reason: Solicitamos que realize correções na submissão seguindo as orientações abaixo: Problema 01) Falta a FOLHA DE APROVAÇÃO (Obrigatório pela ABNT NBR14724) Problema 02) Corrigir a ordem das páginas pré-textuais; a ordem correta (capa, folha de rosto, dedicatória, agradecimentos, epígrafe, resumo

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13

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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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>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 car

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14

Fujinaga, Ichiro. "Adaptive optical music recognition." Thesis, McGill University, 1996. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=42033.

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The basic goal of the Adaptive Optical Music Recognition system presented herein is to create an adaptive software for the recognition of musical notation. The focus of this research has been to create a robust framework upon which a practical optical music recognizer can be built.<br>The strength of this system is its ability to learn new music symbols and handwritten notations. It also continually improves its accuracy in recognizing these objects by adjusting internal parameters. Given the wide range of music notation styles, these are essential characteristics of a music recognizer.<br>The

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15

Kuo, Eric Heng-Shiang 1978. "Assist channel coding for improving optical character recognition." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86453.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.<br>Includes bibliographical references (p. 50).<br>by Eric Heng-Shiang Kuo.<br>S.B.and M.Eng.

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16

Lundqvist, Filip, and Olle Wallberg. "Natural image distortions and optical character recognition accuracy." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187234.

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Current state of the art optical character recognition tools are trained using high quality image datasets. In practical applications, natural images used for character recognition willnot always be of high quality. This report examines the accuracy of a state of the art optical character recognition tool using three distorted natural image datasets. The performed distortions were lossy JPEG compression, contrast reduction and white gaussian noise injection. The accuracy is presented as an average percentage of correct and located text using the Levenshtein distance algorithm. The results indi

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17

Bayless, Mark D. "Improving optical character recognition accuracy for cargo container identification numbers." [Denver, Colo.] : Regis University, 2010. http://adr.coalliance.org/codr/fez/view/codr:139.

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18

Kieri, Andreas. "Context Dependent Thresholding and Filter Selection for Optical Character Recognition." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-197460.

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Thresholding algorithms and filters are of great importance when utilizing OCR to extract information from text documents such as invoices. Invoice documents vary greatly and since the performance of image processing methods when applied to those documents will vary accordingly, selecting appropriate methods is critical if a high recognition rate is to be obtained. This paper aims to determine if a document recognition system that automatically selects optimal processing methods, based on the characteristics of input images, will yield a higher recognition rate than what can be achieved by a m

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19

Dolci, Beatrice. "Development of a Deep Learning system for Optical Character Recognition." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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This thesis is a documentation of the work resulting of an internship in Datalogic USA Inc. in Pasadena, California, of the duration of six months. The developed project had the purpose of designing a system for detecting text lines within high resolution images in an industrial framework, making use of Deep Learning and convolutional neural networks, focusing on the use of a general objects detection system in the context of optical character recognition. The chosen general purpose object detector was YOLO, currently providing state-of-the-art performances in terms of trade-off between speed

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20

Muller, Neil Leonard. "Image recognition using the Eigenpicture Technique (with specific applications in face recognition and optical character recognition)." Master's thesis, University of Cape Town, 1998. http://hdl.handle.net/11427/14381.

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Includes bibliographical references.<br>In the first part of this dissertation, we present a detailed description of the eigenface technique first proposed by Sirovich and Kirby and subsequently developed by several groups, most notably the Media Lab at MIT. Other significant contributions have been made by Rockefeller University, whose ideas have culminated in a commercial system known as Faceit. For a different techniques (i.e. not eigenfaces) and a detailed comparison of some other techniques, the reader is referred to [5]. Although we followed ideas in the open literature (we believe there

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21

Bult, Timothy Paul. "Schema labelling applied to hand-printed Chinese character recognition." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/26175.

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Hand-printed Chinese character recognition presents an interesting problem for Artificial Intelligence research. Input data in the form of arrays of pixel values cannot be directly mapped to unique character identifications because of the complexity of the characters. Thus, intermediate data structures are necessary, which in turn lead to a need to represent knowledge of the characters' composition. Building the intermediate constructs for these hand-printed characters necessarily involves choices among ambiguities, the set of which is so large that an efficient search algorithm becomes centra

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22

Hanson, Adam. "Character recognition of optically blurred textual images using moment invariants /." Online version of thesis, 1993. http://hdl.handle.net/1850/11748.

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23

Walvoord, Derek J. "Advanced correlation-based character recognition applied to the Archimedes Palimpsest /." Online version of thesis, 2008. http://hdl.handle.net/1850/7064.

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24

Kirkpatrick, Michael Gorden. "Optical character recognition : an approach using self- adjusting segmentation of a matrix." Virtual Press, 1997. http://liblink.bsu.edu/uhtbin/catkey/1048390.

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The problem of optical pattern recognition is a broad one. It ranges from identifying shapes in aerial photographs to recognizing letters in hand or machine printed words. This thesis examines many of the issues relating to pattern recognition and, specifically, those pertaining to the optical recognition of characters. It discusses several approaches to various parts of the problem as an illustration of the variety of methods of attack. Some of the particular strengths and weaknesses of those approaches are discussed as well. Finally, a new method of approaching OCR is introduced, developed,

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25

Elliott, Steven C. "Context sensitive optical character recognition using neural networks and hidden Markov models /." Online version of thesis, 1992. http://hdl.handle.net/1850/11054.

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26

Kotwal, Thomas (Thomas Prabhakar Pramod) 1978. "The untrusted computer problem and camera based authentication using optical character recognition." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87272.

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27

Aula, Lara. "Improvement of Optical Character Recognition on Scanned Historical Documents Using Image Processing." Thesis, Högskolan i Gävle, Datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-36244.

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As an effort to improve accessibility to historical documents, digitization of historical archives has been an ongoing process at many institutions since the origination of Optical Character Recognition. The old, scanned documents can contain deteriorations acquired over time or caused by old printing methods. Common visual attributes seen on the documents are variations in style and font, broken characters, ink intensity, noise levels and damage caused by folding or ripping and more. Many of these attributes are disfavoring for modern Optical Character Recognition tools and can lead to failed

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28

Mishra, Vishal Vijayshankar. "Sequence-to-Sequence Learning using Deep Learning for Optical Character Recognition (OCR)." University of Toledo / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1513273051760905.

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29

Avila, Manuel. "Optimisation de modèles markoviens pour la reconnaissance de l'écrit." Rouen, 1996. http://www.theses.fr/1996ROUES034.

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Cette thèse traite de l'optimisation de modèles markoviens dédiés à la reconnaissance de textes manuscrits, dans le cas particulier d'une application à vocabulaire réduit : la lecture des montants littéraux de chèques. Le premier chapitre décrit brièvement les techniques utilisées pour la reconnaissance de l'écrit. Nous présentons également les descriptions des mots que nous avons utilisées. Le second chapitre présente les modèles de Markov cache. Nous présentons notamment les différents niveaux de représentation du problème de la lecture de l'écrit dans le cas de modélisations markoviennes :

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30

Andrews, Robert J. "Optical flow from color image sequences /." St. Lucia, Qld, 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe17830.pdf.

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31

Rodrigues, Antonio Jose Nunes Navarro. "A robust off-line hand written character recognition system using dynamic features." Thesis, University of Newcastle Upon Tyne, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295503.

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32

Seni, Giovanni. "Large vocabulary recognition of on-line handwritten cursive words." Buffalo, N.Y. : Dept. of Computer Science, State University of New York at Buffalo, 1995. http://www.cse.buffalo.edu/tech%2Dreports/95%2D38.ps.Z.

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33

Kozlovski, Nikolai. "TEXT-IMAGE RESTORATION AND TEXT ALIGNMENT FOR MULTI-ENGINE OPTICAL CHARACTER RECOGNITION SYSTEMS." Master's thesis, University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3607.

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Previous research showed that combining three different optical character recognition (OCR) engines (ExperVision® OCR, Scansoft OCR, and Abbyy® OCR) results using voting algorithms will get higher accuracy rate than each of the engines individually. While a voting algorithm has been realized, several aspects to automate and improve the accuracy rate needed further research. This thesis will focus on morphological image preprocessing and morphological text restoration that goes to OCR engines. This method is similar to the one used in restoration partial finger prints. Series of morphol

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34

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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35

Stine, William V. "A study in applying optical character recognition technology for the Foreign Broadcast Information Service field bureaus." Master's thesis, This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-03172010-020135/.

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36

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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37

Shah, Jaimin Nitesh. "Underwater Document Recognition." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1619452066101887.

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38

Chen, Yong. "Constructing a language model based on data mining techniques for a Chinese character recognition system /." View the Table of Contents & Abstract, 2004. http://sunzi.lib.hku.hk/hkuto/record/B30708527.

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39

Chen, Yong, and 陳勇. "Constructing a language model based on data mining techniques for a Chinese character recognition system." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B44570193.

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40

He, Tingting, and 何婷婷. "A study on several problems in online handwritten Chinese character recognition." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B42182086.

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41

Rashid, Sheikh Faisal [Verfasser], and Thomas [Akademischer Betreuer] Breuel. "Optical Character Recognition - A Combined ANN/HMM Approach / Sheikh Faisal Rashid. Betreuer: Thomas Breuel." Kaiserslautern : Technische Universität Kaiserslautern, 2014. http://d-nb.info/1063331005/34.

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42

Radvar-Zanganeh, Siasb. "The role of the Elementary Perceiver and Memorizer (EPAM) in optical character recognition (OCR)." Thesis, Connect to online version, 1994. http://0-wwwlib.umi.com.mercury.concordia.ca/cr/concordia/fullcit?pMM10888.

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43

Favish, Ashleigh. "Data Capture Automation in the South African Deeds Registry using Optical Character Recognition (OCR)." Master's thesis, Faculty of Commerce, 2019. http://hdl.handle.net/11427/31389.

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The impact of apartheid on land registration is still evident within South Africa. The Deeds Registry is facing a current backlog in registering an estimated 900,000 title deeds. Providing formal ownership, through title, is seen as necessary for unlocking the 'dead capital’ of unregistered property, fostering access to capital markets and poverty alleviation. Within the current legislative framework, the Deeds Registry only accepts paper documents, which introduces inefficiencies. To increase the number of deeds processed per day, automation of manual data capture is tested using an OCR pipel

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44

Lindqvist, Emil Gedda &amp Kalle. "Genetically modelled Artificial Neural Networks for Optical Character Recognition : An evaluation of chromosome encodings." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4504.

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Context. Custom solutions to optical character recognition problems are able to reach higher recognition rates then a generic solution by their ability to exploiting the limitations in the problem domain. Such solutions can be generated with genetic algorithms. This thesis evaluates two different chromosome encodings on an optical character recognition problem with a limited problem domain. Objectives. The main objective for this study is to compare two different chromosome encodings used in a genetic algorithm generating neural networks for an optical character recognition problem to evaluate

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45

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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46

Du, Daqiao. "Neural network character recognition with a 2-D Fourier transform preprocessor." PDXScholar, 1991. https://pdxscholar.library.pdx.edu/open_access_etds/4200.

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In pattern recognition applications, it is usually important that the same identification be given for a pattern, independent of a variety of positions, rotations and /or distortions of the pattern within the recognition device's field of view. This research relates to development of a preprocessor for a neural network character recognition system, where the role of the preprocessor is to assist in minimizing the difficulties related to variations of position and rotations of a character within the field of view. The preprocessor explored here was suggested in 1970' (Lendaris & Stanly, 1970),

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47

Kuan, Sin Wo. "VLSI implementation of neural network for character recognition application." Ohio : Ohio University, 1992. http://www.ohiolink.edu/etd/view.cgi?ohiou1172783946.

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48

Sprague, Christopher. "AUTONOMOUS REPAIR OF OPTICAL CHARACTER RECOGNITION DATA THROUGH SIMPLE VOTING AND MULTI-DIMENSIONAL INDEXING TECHNIQUES." Master's thesis, University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2642.

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The three major optical character recognition (OCR) engines (ExperVision, Scansoft OCR, and Abby OCR) in use today are all capable of recognizing text at near perfect percentages. The remaining errors however have proven very difficult to identify within a single engine. Recent research has shown that a comparison between the errors of the three engines proved to have very little correlation, and thus, when used in conjunction, may be useful to increase accuracy of the final result. This document discusses the implementation and results of a simple voting system designed to prove the hypothesi

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49

Liang, Jisheng. "Document structure analysis and performance evaluation /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/6043.

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50

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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