Dissertations / Theses on the topic 'Character recogniion'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Character recogniion.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
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.
Full textWong, 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.
Full text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.
Full textWong, 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.
Full textAn, Kyung Hee. "Concurrent Pattern Recognition and Optical Character Recognition." Thesis, University of North Texas, 1991. https://digital.library.unt.edu/ark:/67531/metadc332598/.
Full textAbdelRaouf, Ashraf M. "Offline printed Arabic character recognition." Thesis, University of Nottingham, 2012. http://eprints.nottingham.ac.uk/12601/.
Full textCowell, J. R. "Character recognition in unconstrained environments." Thesis, Nottingham Trent University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.277696.
Full textALVARENGA, 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.
Full textCOORDENAÇÃO 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.
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.
Full text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.
Full textSantos, Claudio Filipi Gonçalves dos. "Optical character recognition using deep learning." Universidade Estadual Paulista (UNESP), 2018. http://hdl.handle.net/11449/154100.
Full textRejected 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 na língua vernácula, resumo em língua estrangeira, listas de ilustrações, de tabelas, de abreviaturas, de siglas e de símbolos e sumário). Problema 03) Faltam as palavras-chave no resumo e no abstracts. Na página da Seção de pós-graduação, em Instruções para Qualificação e Defesas de Dissertação e Tese, você pode acessar o modelo das páginas pré-textuais. Lembramos que o arquivo depositado no repositório deve ser igual ao impresso, o rigor com o padrão da Universidade se deve ao fato de que o seu trabalho passará a ser visível mundialmente. Agradecemos a compreensão. on 2018-05-24T20:59:53Z (GMT)
Submitted by Claudio Filipi Gonçalves dos Santos (cfsantos85@gmail.com) on 2018-05-25T00:43:19Z No. of bitstreams: 1 optical-character-recognition-16052018.pdf: 11084990 bytes, checksum: 6f8d7431cd17efd931a31c0eade10c65 (MD5)
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) A paginação deve ser sequencial, iniciando a contagem na folha de rosto e mostrando o número a partir da introdução, a ficha catalográfica ficará após a folha de rosto e não deverá ser contada. Problema 03) Na descrição do item: Título em outro idioma – Se você colocou no título em inglês deve por neste campo o título em outro idioma (ex: português, espanhol, francês...) Estamos encaminhando via e-mail o template/modelo para que você possa fazer as correções. Lembramos que o arquivo depositado no repositório deve ser igual ao impresso, o rigor com o padrão da Universidade se deve ao fato de que o seu trabalho passará a ser visível mundialmente. Agradecemos a compreensão. on 2018-05-25T15:22:45Z (GMT)
Submitted by Claudio Filipi Gonçalves dos Santos (cfsantos85@gmail.com) on 2018-05-25T15:52:53Z No. of bitstreams: 1 optical-character-recognition-16052018.pdf: 11089966 bytes, checksum: d6c863077a995bd2519035b8a3e97c80 (MD5)
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) Agradecemos a compreensão. on 2018-05-25T18:03:19Z (GMT)
Submitted by Claudio Filipi Gonçalves dos Santos (cfsantos85@gmail.com) on 2018-05-25T18:08:09Z No. of bitstreams: 1 Claudio Filipi Gonçalves dos Santos Corrigido Biblioteca.pdf: 8257484 bytes, checksum: 3a61ebfa8e1d16c9d0c694f46b979c1f (MD5)
Approved for entry into archive by Elza Mitiko Sato null (elzasato@ibilce.unesp.br) on 2018-05-25T18:51:24Z (GMT) No. of bitstreams: 1 santos_cfg_me_sjrp.pdf: 8257484 bytes, checksum: 3a61ebfa8e1d16c9d0c694f46b979c1f (MD5)
Made available in DSpace on 2018-05-25T18:51:24Z (GMT). No. of bitstreams: 1 santos_cfg_me_sjrp.pdf: 8257484 bytes, checksum: 3a61ebfa8e1d16c9d0c694f46b979c1f (MD5) Previous issue date: 2018-04-26
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Detectores óticos de caracteres, ou Optical Character Recognition (OCR) é o nome dado à técnologia de traduzir dados de imagens em arquivo de texto. O objetivo desse projeto é usar aprendizagem profunda, também conhecido por aprendizado hierárquico ou Deep Learning para o desenvolvimento de uma aplicação com a habilidade de detectar áreas candidatas, segmentar esses espaços dan imagem e gerar o texto contido na figura. Desde 2006, Deep Learning emergiu como uma nova área em aprendizagem de máquina. Em tempos recentes, as técnicas desenvolvidas em pesquisas com Deep Learning têm influenciado e expandido escopo, incluindo aspectos chaves nas área de inteligência artificial e aprendizagem de máquina. Um profundo estudo foi conduzido com a intenção de desenvolver um sistema OCR usando apenas arquiteturas de Deep Learning.A evolução dessas técnicas, alguns trabalhos passados e como esses trabalhos influenciaram o desenvolvimento dessa estrutura são explicados nesse texto. Essa tese demonstra com resultados como um classificador de caracteres foi desenvolvido. Em seguida é explicado como uma rede neural pode ser desenvolvida para ser usada como um detector de objetos e como ele pode ser transformado em um detector de texto. Logo após é demonstrado como duas técnicas diferentes de Deep Learning podem ser combinadas e usadas na tarefa de transformar segmentos de imagens em uma sequência de caracteres. Finalmente é demonstrado como o detector de texto e o sistema transformador de imagem em texto podem ser combinados para se desenvolver um sistema OCR completo que detecta regiões de texto nas imagens e o que está escrito nessa região. Esse estudo demonstra que a idéia de usar apenas estruturas de Deep Learning podem ter performance melhores do técnicas baseadas em outras áreas da computação como por exemplo o processamento de imagens. Para detecção de texto foi alcançado mais de 70% de precisão quando uma arquitetura mais complexa foi usada, por volta de 69% de traduções de imagens para texto corretas e por volta de 50% na tarefa ponta-à-ponta de detectar as áreas de texto e traduzi-las em sequência de caracteres.
Optical Character Recognition (OCR) is the name given to the technology used to translate image data into a text file. The objective of this project is to use Deep Learning techniques to develop a software with the ability to segment images, detecting candidate characters and generating textthatisinthepicture. Since2006,DeepLearningorhierarchicallearning, emerged as a new machine learning area. Over recent years, the techniques developed from deep learning research have influenced and expanded scope, including key aspects of artificial intelligence and machine learning. A thorough study was carried out in order to develop an OCR system using only Deep Learning architectures. It is explained the evolution of these techniques, some past works and how they influenced thisframework’sdevelopment. Inthisthesisitisdemonstratedwithresults how a single character classifier was developed. Then it is explained how a neural network can be developed to be an object detector and how to transform this object detector into a text detector. After that it shows how a set of two Deep Learning techniques can be combined and used in the taskoftransformingacroppedregionofanimageinastringofcharacters. Finally, it demonstrates how the text detector and the Image-to-Text systemswerecombinedinordertodevelopafullend-to-endOCRsystemthat detects the regions of a given image containing text and what is written in this region. It shows the idea of using only Deep Learning structures can outperform other techniques based on other areas like image processing. In text detection it reached over 70% of precision when a more complex architecture was used, around 69% of correct translation of image-to-text areasandaround50%onend-to-endtaskofdetectingareasandtranslating them into text.
1623685
Duewer, Trent A. "Research in Japanese optical character recognition." Full text, Acrobat Reader required, 1998. http://viva.lib.virginia.edu/etd/these/duewer98.pdf.
Full textWong, Pak-kwong. "Multifont printed Chinese character recognition system /." [Hong Kong : University of Hong Kong], 1991. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13068556.
Full textMalyan, R. R. "Machine learning for handprinted character perception." Thesis, Kingston University, 1989. http://eprints.kingston.ac.uk/20527/.
Full textSandgren, Frida. "Creation of a customised character recognition application." Thesis, Uppsala University, Department of Linguistics and Philology, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4801.
Full textThis master’s thesis describes the work in creating a customised optical character recognition (OCR) application; intended for use in digitisation of theses submitted to the Uppsala University in the 18th and 19th centuries. For this purpose, an open source software called Gamera has been used for recognition and classification of the characters in the documents. The software provides specific algorithms for analysis of heritage documents and is designed to be used as a tool for creating domain-specific (i.e. customised) recognition applications.
By using the Gamera classifier training interface, classifier data was created which reflects the characters in the particular theses. The data can then be used in automatic recognition of ‘new’ characters, by loading it into one of Gamera’s classifiers. The output of Gamera are sets of classified glyphs (i.e. small images of characters), stored in an XML-based format.
However, as OCR typically involves translation of images of text into a machine-readable format, a complementary OCR-module was needed. For this purpose, an external Gamera module for page segmentation was modified and used.
In addition, a script for control of the OCR-process was created, which initiates the page segmentation on Gamera classified glyphs. The result is written to text files.
Finally, in a test for recognition accuracy, one of the theses was used for creation of training data and for test of data. The result from the test show an average accuracy rate of 82% and that there is a need for a better pre-processing module which removes more noise from the images, as well as recognises different character sizes in the images before they are run by the OCR-process.
Viklund, Alexander, and Emma Nimstad. "Character Recognition in Natural Images Utilising TensorFlow." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208385.
Full textDet är vanligt att använda konvolutionära artificiella neuronnät (CNN) för bildigenkänning, då de ger de minsta felmarginalerna på kända datamängder som SVHN och MNIST. Dock saknas det forskning om användning av CNN för klassificering av bokstäver i naturliga bilder när det gäller hela det engelska alfabetet. Detta arbete beskriver ett experiment där TensorFlow används för att bygga ett CNN som tränas och testas med bilder från Chars74K. 15 bilder per klass används för träning och 15 per klass för testning. Målet med detta är att uppnå högre noggrannhet än 55.26%, vilket är vad de campos et al. [1] uppnådde med en metod utan artificiella neuronnät. I rapporten utforskas olika tekniker för att artificiellt utvidga den lilla datamängden, och resultatet av att applicera rotation, utdragning, translation och bruspåslag utvärderas. Resultatet av det är att alla dessa metoder utom bruspåslag ger en positiv effekt på nätverkets noggrannhet. Vidare visar experimentet att med ett CNN med tre lager går det att skapa en bokstavsklassificerare som är lika bra som de Campos et al.s klassificering. Om fler experiment skulle genomföras på nätverkets och utvidgningens parametrar är det troligt att ännu bättre resultat kan uppnås.
Granlund, Oskar, and Kai Böhrnsen. "Improving character recognition by thresholding natural images." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208899.
Full textDagens optisk teckeninläsnings (OCR) algoritmer är kapabla av att extrahera text från bilder inom fördefinierade förhållanden. De moderna metoderna har uppnått en hög träffsäkerhet för maskinskriven text med minimala förvrängningar, men bilder tagna i en naturlig scen är fortfarande svåra att hantera. De senaste åren har ett stort intresse för att förbättra tecken igenkännings algoritmerna uppstått, eftersom fler kraftfulla och handhållna enheter används. Det huvudsakliga problemet när det kommer till igenkänning i naturliga bilder är olika förvrängningar som infallande ljus, textens textur och komplicerade bakgrunder. Olika metoder för förbehandling och därmed separation av texten och dess bakgrund har studerats under den senaste tiden. I våran studie bedömer vi förbättringen som uppnås vid förbehandlingen med två metoder som kallas för k-means och Otsu genom att jämföra svaren från en OCR algoritm. Studien visar att Otsu och k-means kan förbättra träffsäkerheten i vissa förhållanden men generellt sett ger det ett sämre resultat än de oförändrade bilderna.
Chai, Sin-Kuo. "Multiclassifier neural networks for handwritten character recognition." Ohio : Ohio University, 1995. http://www.ohiolink.edu/etd/view.cgi?ohiou1174331633.
Full textClarke, Eddie. "A novel approach to handwritten character recognition." Thesis, University of Nottingham, 1995. http://eprints.nottingham.ac.uk/14035/.
Full textKordi, Kamran. "Intelligent character recognition using hidden Markov models." Thesis, Loughborough University, 1990. https://dspace.lboro.ac.uk/2134/13786.
Full textBanks, R. N. "Neural networks for hand-printed character recognition." Thesis, University of Nottingham, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293655.
Full textRyan, Matthew Stephen. "Dynamic character recognition using Hidden Markov Models." Thesis, University of Warwick, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263326.
Full textRichardson, Fiona Mary. "Dynamic representations in character production and recognition." Thesis, University of Hertfordshire, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.289606.
Full textHU, MARIE N. "A STUDY OF CHINESE CHARACTER RECOGNITION METHODS." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1035813506.
Full textBayless, 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.
Full textHanson, Adam. "Character recognition of optically blurred textual images using moment invariants /." Online version of thesis, 1993. http://hdl.handle.net/1850/11748.
Full text林依民 and Yi-min Lin. "Computer recognition of printed Chinese characters." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1990. http://hub.hku.hk/bib/B31209919.
Full text梁祥海 and Cheung-hoi Leung. "Computer recognition of handprinted Chinese characters." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1986. http://hub.hku.hk/bib/B31230660.
Full text施雷 and Lui Sze. "Computer recognition of printed Chinese characters." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31213601.
Full textLeung, Cheung-hoi. "Computer recognition of handprinted Chinese characters /." [Hong Kong : University of Hong Kong], 1986. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12322131.
Full textLundqvist, 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.
Full textNuvarande moderna verktyg för optisk teckenigenkänning tränas med bilder av hög kvalité. I praktiska situationer kommer naturliga bilder som används för optisk teckenigenkänning inte alltid vara av hög kvalité. Denna rapport använder tre förvrängda datauppsättningar av naturliga bilder för att utvärdera träffsäkerheten hos ett modernt verktyg för optiskteckenigenkänning. De utförda förvrängningarna var förstörande JPEG komprimering, kontrastreducering och injektion av vitt gaussiskt brus. Träffsäkerheten presenteras som en genomsnittlig procentenhet av korrekt och lokaliserad text genom användning av algoritmen Levenshteinavstånd. Resultaten indikerar att injektion av vitt gaussiskt brus försämrade träffsäkerheten hos optisk teckenigenkänning avsevärt. Vidare hade förstörande JPEG komprimering och kontrastreducering en liknande, men mindre, effekt.
遲秉壯 and Ping-chong Chee. "Hand-printed Chinese character recognition and image preprocessing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31213972.
Full textKuo, 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.
Full textIncludes bibliographical references (p. 50).
by Eric Heng-Shiang Kuo.
S.B.and M.Eng.
Lin, Yeu-Chang, and 林裕章. "Using Confusion Characters to Improve Character Recognition Rate." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/34907033760970274876.
Full text國立交通大學
資訊工程學系
84
This thesis proposes a confusion set analysis approach to improve character recognition rate. We collect all confused characters of a character by analyzing the recognition results of 100 training samples taken from the CCL/HCCR database. In the training phase, a character is first recognized by an OCR system OCR1 and top five candidates are outputted. Each candidate of the recognition results is assigned a weight in reversed-rank. After all training samples of all 5401 characters are trained, we create a confusion set for each character. If the reversed-rank sum of an output candidate is greater than a threshold, the input character is stored in the confusion set of the output candidate. Similar characters in a confusion set are different in certain parts. We select features in these distinct parts to distinguish the similar characters. However, it is hard to select suitable features when the size of a confusion set is large. We cluster the characters of the confusion set into subgroups by using crossing count features. Then, we calculate the weights for all features among the characters in each subgroup to select the most distinguishable features. In the recognition phase, we obtain the most possible output character by the OCR system OCR1 for each input character. The characters in the confusion set with respect to the output character are recognized by another OCR system OCR2 to find the final recognition result. Experimental results show that the hit rates of the subgroups of the confusion sets are 93. 76% and the average size of the subgroups is 9.01. The final recognition rate for our system is 86.97%.
Liang, Hao-Po, and 梁浩伯. "The Action Research of Direct Teaching Chinese Characters to Preschool Children’s Character Recognition." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/74178509709454582854.
Full text國立臺中教育大學
教育學系
102
This research used action research, discuss the implementation of direct teaching Chinese Characters of San Zi Jing. In the process, trying to development variety of teaching strategies, and promote the quantity of Chinese character recognition. The subjects of the study were 24 children in kindergarten of Changhua County. Implementation of ten weeks, five times a week, every thirty minutes of direct teaching Chinese characters courses. In the course of study, the researcher conducted data collection and analysis through literacy scale, teachers’ interviews, observation records, teaching notes, parents surveys, parents qualitative feedbacks, course records, summarized the conclusions of this study are as follows: 1. Let preschool to read aloud texts, recite texts, master the voice of the texts is the first stage of direct teaching Chinese characters; Secondly , establishing a link of Chinese characters pronunciation and shape with variety of teaching strategies is the next stage of direct teaching Chinese characters. 2. Direct teaching Chinese characters can effectively promote the quantity of Chinese character recognition and parents’ affirmative generally. 3. Difficulties encountered in the process have: the degree of dedication in reading texts, the interference of teaching, the appropriate of seating arrangements, checklist effectiveness of these course, the particularity of the subjects. Based on the above conclusions, researcher suggestions on teaching practice, including suggestions for teachers, parents and suggestions to recommend to kindergarten, as well as recommendations on the follow-up study. Providing education conducted Chinese character teaching partners and researchers by direct teaching Chinese characters refer to and apply.
Tai, Yu-Ho, and 戴宇核. "The Effect of Character-Based Teaching Materials on Chinese Characters’ Recognition and Writing." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/7zpw27.
Full text國立臺灣師範大學
華語文教學系
106
Recently, the method of teaching Chinese Characters has drawn great attention in the field of Chinese Teaching. Studies on different teaching methods and strategies have become hot research issues. How to improve the reading(recognition) and writing ability of students at primary level is considered greatly by teachers and researchers. Referring to the theory of serial position effect, this study introduced Chinese Characters input and output path models. At the same time, the teaching strategies were divided into two groups, that is, Pronunciation as priority group and Writing as priority group. There is no previous exploration on whether teaching pronunciation first or writing first can mostly benefit Chinese Characters’ reading(recognition) ability and(or) writing ability. Therefore, the researcher used the teaching strategies based on the two models as mentioned before as well as the traditional teaching method of sentence-based teaching to design the research and conduct the empirical research. The participants of this research were 177 beginning level learners of Chinese language, including Filipino-Chinese and non-Filipino-Chinese, in 8th grade at Grace Christian College, Philippines. After analyzing the data, three conclusions were obtained as follows: 1.The learners who accepted the character-based approach improved their ability of reading(recognition) and writing Chinese Characters more than those accepted sentence-based approach learners. 2.The results of this research consisted with the primary effect theory of serial position effect. The ability of Chinese Characters’ reading(recognition) of students in the Pronunciation as priority group was significantly higher than that of students in other groups. While the ability of Chinese Characters’ writing of students in Writing as priority group was significantly higher than that of students in other groups. 3.In general, the Filipino-Chinese students’ ability of reading(recognition) and writing was significantly higher than that of non-Filipino-Chinese students. This study may provide a reference for teachers and researchers in Chinese teaching practice.
Jian, Jia-Ching, and 簡嘉慶. "Very High Precision Optical Character Recognition For Clean-Fixed-Sized True Type Characters." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/29nd45.
Full text國立中央大學
資訊工程學系
105
Optical Character Recognition has been studied for many years. However, no OCR tools can claim 100% recognition rate because of the variation in image quality, documentation layout, character fonts and sizes. When there are changes in one of them, recognition rate is often greatly impacted. Korat is an image-based test regression tool developed in our lab. Korat captures the screen image from a system under test. Therefore, the image is clean, no noises, and no rotation. Based on this condition, we do not deal with situations like image noises, which makes OCR a difficult problem in this thesis. In Korat's practical applications, nearly 100% recognition rate is often required. So even there are many existing OCR tools with 88-95% recognition rate, they do not meet the requirements of Korat's practical applications. In this research, we combine the template matching and dynamic programming algorithm to find the optimal solution with the smallest sum of remaining pixels in all possible combinations of recognition so that the recognition rate could be achieved 100% at nearly.
"Video-based handwritten Chinese character recognition." 2003. http://library.cuhk.edu.hk/record=b6073522.
Full text"June 2003."
Thesis (Ph.D.)--Chinese University of Hong Kong, 2003.
Includes bibliographical references (p. [114]-130).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Mode of access: World Wide Web.
Abstracts in English and Chinese.
Wu, Hsien, and 吳嫻. "The homophonic effect in word recognition processes of Chinese single characters and two-character words." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/16357920955111739503.
Full textLi, Yu-An, and 李育安. "Handwritten and Printed Chinese Character Recognition By Using Computer Font Type Chinese Characters into Convolutional Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/99ad3j.
Full text國立臺灣大學
工程科學及海洋工程學研究所
106
The main purpose of this paper is to improve Handwritten Chinese Character Recognition and traditional, non-modern Printed Chinese Character Recognition problem. By using the existing different style of Chinese font resources in computer system and online sources, we take most commonly used 5000 and 10000 words, then do several data deformation and preprocessing by image processing skills to produce training data. Combined with the technology of Convolutional Neural Networks in machine learning, we trained a distinguished model which can be used to recognize handwritten and printed Chinese character both. The main goal of this paper is to find the valid training features, optimize parameters and fine tune our model to get a better performance. The results of this paper mainly include: (1) How to train a model which can recognize both the handwritten font and the printed font simultaneously on by existing computer word font. (2) For the printed Chinese character font, we mainly focus on early traditional printed fonts, and improves the recognition problems, such as rare Chinese characters recognition and characters easily damaged or blur in the original text. (3) We conduct our experiments with the Beijing Civil News, the Biansha Tibetan Buddhist Dharma and the 2013 CASIA handwritten Chinese character public test set. The results show that the model and method we proposed in this paper can reach the accuracy of 69.9% on News, 89.29% on Buddhist Dharma, and 58.27% on handwriting testing set. Compared with the existing common OCR recognition software, our model can improve the accuracy about 2~3%. Key Word : HCCR、PCCR、Image Processing、Machine Learning、Convolutional Neural Networks
"Perspectives of pattern recognition in handwritten character recognition." Tulane University, 1995.
Find full textacase@tulane.edu
"On-line Chinese character recognition." 1997. http://library.cuhk.edu.hk/record=b1962412.
Full textThesis (Ph.D.)--Chinese University of Hong Kong, 1997.
Includes bibliographical references (p. 183-196).
Microfiche. Ann Arbor, Mich.: UMI, 1998. 3 microfiches ; 11 x 15 cm.
Chen, Ching-Yi, and 陳慶逸. "Off-line Handwritten Character Recognition." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/78087161407572953101.
Full text淡江大學
電機工程學系研究所
86
In this thesis , we propose a new scheme for off-line recognition of totallyun constrained handwritten characters using SOM/LVQ neural networks and extractio ngfeature vectors by kirsch masks method. In the learning phase, the SOM neura l networks is used to cluster the feature vectors into several classes. In the recognitionstage, the learning results of the neural networks are utilized to identify the inputdata. In order to seek the optium cluster set, the resultin g clusters from the SOMneural networks need to be refined such that the hetero geneity among different targetscan be increased. This is done by introducing a supervised refining algorithm. We have chosen the supervied version of Kohone n''s model known as the Learning vector Quantization to refine selected feature s.The proposed scheme consists of two stages: a feature extraction stage for e xtractingfour-directional local feature vectors with Kirsch masks and one glob al feature vectorform compution the density over small regions of the image, a nd a classification stagefor recognizing characters with SOM neural networks. We first use the Kohonen clusteringnetworks (SOM) to represent the training da ta with minimum quantization error whilemaximizes the within-target homogeneit y. We then use the LVQ to learn the between-targetheterogeneity. It is done by collecting those selected neurons as the inital cluster centers for the LVQ t o learn their class boundaries. This is maximize the probability of correct cl assification. In order to verify the performance of the proposed approach, 630 0 handwritten characters written by 70 persons were collected as the database, 2000characters are used as the training set and the other 4300 characters as the testing set.Some experimental results are conducted to show the feasibilit y of our proposed method.
Tzeng, Yi Tyng, and 曾宜婷. "The Effects of Spokes-character's Cuteness, Narrative and Character-product Congruence on Consumer's Attitude and Recognition." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/25099231929730491299.
Full textGhabrial, Melad Y. "Parallel algorithms for handwritten character recognition." Thesis, 1990. http://spectrum.library.concordia.ca/3859/1/MM97702.pdf.
Full textHsiao, Hua-Ling, and 蕭慧琳. "Orthographic processing in Chinese Character Recognition." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/xkqy95.
Full text國立中央大學
認知與神經科學研究所
94
This research explored the role of radical and radical position in Chinses character recognition. The constituents of Chinese character orthography shall cover at least the character structure, the radicals, and the radical positions. Both behavioral and electrophisological approaches were adopted to tackle the orthographic processing issue. In experiment 1, different tasks and SOA were used to find the priming effects during the manipulations of radical positions. The prime is one of the radicals belong to target character, but there is no significant priming effect. When the prime’s structure is the same as the target’s structure, in experiment 2, the prime and the target shared one radical. There were facilitated priming effects when the same radical at the same position. If the prime is the radical combined with one low frequency radical, and then the subjects were interrupted to process the target character. So the combinability of radicals induced the inhibited priming effects. In experiment 3, through naming task and longer SOA, the facilitated priming effects turned into inhibited priming effects. When subjects process the characters longer, more information than radical position would be processed. In experiment 4, there would be repetition priming effect when the prime and the target were the same characters. If the prime’s radical position reversed, there were still facilitated priming effects of low frequency target characters. The current study shows the effect of radical position processing of Chinese character recognition in different experimental designs. According to the results, there would be more confident hypothesis of Chinsese orthographic rules.
Liu-yuan, Lai, and 賴留圓. "Video Caption Detection and Character Recognition." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/19718241085211897505.
Full text國立交通大學
資訊工程系
91
This thesis focuses on caption detection, extraction and recognition in videos. The proposed method uses the high contrast edges and the closed-form boundaries of characters to detect and located caption regions with high precision. Connect ed component analysis is used subsequently to segment out character components. A distance measure between components is defined to guide the merging of the components from the same character and to filter out non-text noise. Finally, a two -stage classifier is proposed for Chinese video OCR with the ability to tolerate poor image quality and the presence of multiple fonts.
CAI, YU-SHENG, and 蔡玉生. "Handwritten character recognition by graph matching." Thesis, 1988. http://ndltd.ncl.edu.tw/handle/98437928794972396263.
Full textHUANG, YU-KAI, and 黃郁凱. "Recognition of Numerical Character on Scoreboard." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/45656643709376072412.
Full text謝坤融. "Neural network models for character recognition." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/84891320301741280898.
Full text