Tesis sobre el tema "Handwritten"
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Masrour, Mohsen. "Quiz corrector : Handwritten digits recognition". Thesis, Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-21615.
Texto completoAllan, Jonathan. "Automated assessment of handwritten scripts". Thesis, Nottingham Trent University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430258.
Texto completoHiggins, C. A. "Automatic recognition of handwritten script". Thesis, University of Brighton, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.372081.
Texto completoLynch, Kathryn Anne. "Handwritten as an industrial body". The Ohio State University, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=osu1329426394.
Texto completoMendes, Alexandra Sofia Ferreira. "Structured editing of handwritten mathematics". Thesis, University of Nottingham, 2012. http://eprints.nottingham.ac.uk/41239/.
Texto completoMatsakis, Nicholas E. (Nicholas Elias) 1976. "Recognition of handwritten mathematical expressions". Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/16727.
Texto completoIncludes bibliographical references (leaves 58-59).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
In recent years, the recognition of handwritten mathematical expressions has received an increasing amount of attention in pattern recognition research. The diversity of approaches to the problem and the lack of a commercially viable system, however, indicate that there is still much research to be done in this area. In this thesis, I will describe an on-line approach for converting a handwritten mathematical expression into an equivalent expression in a typesetting command language such as TEX or MathML, as well as a feedback-oriented user interface which can make errors more tolerable to the end user since they can be quickly corrected. The three primary components of this system are a method for classifying isolated handwritten symbols, an algorithm for partitioning an expression into symbols, and an algorithm for converting a two-dimensional arrangements of symbols into a typeset expression. For symbol classification, a Gaussian classifier is used to rank order the interpretations of a set of strokes as a single symbol. To partition an expression, the values generated by the symbol classifier are used to perform a constrained search of possible partitions for the one with the minimum summed cost. Finally, the expression is parsed using a simple geometric grammar.
by Nicholas E. Matsakis.
S.B.and M.Eng.
Toledo, Testa Juan Ignacio. "Information extraction from heterogeneous handwritten documents". Doctoral thesis, Universitat Autònoma de Barcelona, 2019. http://hdl.handle.net/10803/667388.
Texto completoEl objetivo de esta tesis es la extracción de Información de documentos total o parcialmente manuscritos, con una cierta estructura. Básicamente trabajamos con dos escenarios de aplicación diferentes. El primer escenario son los documentos modernos altamente estructurados, como los formularios. En estos documentos, la información semántica está pre-definida en campos con una posición concreta en el documento i la extracción de información es equivalente a una transcripción. El segundo escenario son los documentos semi-estructurados totalmente manuscritos, donde, además de transcribir, es necesario asociar un valor semántico, de entre un conjunto conocido de valores posibles, a las palabras manuscritas. En ambos casos, la calidad de la transcripción tiene un gran peso en la precisión del sistema. Por ese motivo proponemos modelos basados en redes neuronales para transcribir el texto manuscrito. Para poder afrontar el reto de los documentos semi-estructurados, hemos generado un benchmark, compuesto de dataset, una serie de tareas y una métrica que fue presentado a la comunidad científica a modo de competición internacional. También proponemos diferentes modelos basados en Redes Neuronales Convolucionales y Recurrentes, capaces de transcribir y asignar diferentes etiquetas semánticas a cada palabra manuscrita, es decir, capaces de extraer información.
The goal of this thesis is information Extraction from totally or partially handwritten documents. Basically we are dealing with two different application scenarios. The first scenario are modern highly structured documents like forms. In this kind of documents, the semantic information is encoded in different fields with a pre-defined location in the document, therefore, information extraction becomes equivalent to transcription. The second application scenario are loosely structured totally handwritten documents, besides transcribing them, we need to assign a semantic label, from a set of known values to the handwritten words. In both scenarios, transcription is an important part of the information extraction. For that reason in this thesis we present two methods based on Neural Networks, to transcribe handwritten text.In order to tackle the challenge of loosely structured documents, we have produced a benchmark, consisting of a dataset, a defined set of tasks and a metric, that was presented to the community as an international competition. Also, we propose different models based on Convolutional and Recurrent neural networks that are able to transcribe and assign different semantic labels to each handwritten words, that is, able to perform Information Extraction.
Fan, Rong. "Recognition of dates handwritten on cheques". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ39986.pdf.
Texto completoOliveira, Luiz Eduardo Soares. "Automatic recognition of handwritten numerical strings". Mémoire, Montréal : École de technologie supérieure, 2003. http://wwwlib.umi.com/cr/etsmtl/fullcit?pNQ85289.
Texto completo"Thesis presented to the École de technologie supérieure in partial fulfillment of the thesis requirement for the degree of philosophiae doctor in engineering". La numérotation de cet ouvrage est erronée. Bibliogr.: f. [149]-163. Également disponible en version électronique.
Fang, Bin y 房斌. "Verification of off-line handwritten signatures". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31241645.
Texto completoFREIXINHO, MARIA ANGELICA PEREIRA. "HANDWRITTEN DIGITS RECOGNITION BY NEURAL NETWORKS". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1996. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9017@1.
Texto completoEsta dissertação investiga a utilização de Redes Neurais Artificiais (RNAs) na área de reconhecimento de caracteres, em particular de dígitos manuscritos. Nesta investigação foram utilizadas amostras reais de dígitos isolados e de códigos postais brasileiros relativos e vários escritores. O trabalho consiste de quatro partes principais: o estudo das metodologias de reconhecimento e da semântica e estrutura de representação de caracteres; o desenvolvimento das etapas de pré-processamento dos dígitos; o desenvolvimento das RNAs para o reconhecimento de dígitos manuscritos; e o estudo de casos. No estudo sobre a metodologia de reconhecimento de caracteres fez-se um levantamento preliminar das diversas aplicaões de sistemas OCR (Optical Character Recognition). Enfatizou-se a classificação dos diversos tipos de semânticas existentes de acordo com a aplicação específica, bem como a estrutura geral de um sistema OCR. O estudo também consistiu da análise e apresentação de modelos convencionais e de sistemas inteligentes na implementação da etapa de classificação dos sistemas OCR. O desenvolvimento do pré-processamento dos dígitos envolveu um extenso estudo bibliográfico de diversas metodologias para cada uma de suas etapas. Foram estudados os algoritmos mais empregados nas etapas de pré- processamento de um sistema. OCR: conversão de níveis de cinza para representação binária ( thresholding), filtragem, segmentação e normalização. A partir desse estudo, foram selecionados e desenvolvidos determinados tipos de algoritmos para o pré-processamento. No desenvolvimento de RNAs para o reconhecimento de dígitos manuscritos fez-se uma investigação de diversas metodologias, incluindo as arquiteturas e os algoritmos de aprendizado mais empregados. Neste estudo, constatou-se a predominância do uso do algoritmo de retropropagação do erro (BackPropagation) para o treinamento das redes nas aplicações de reconhecimento de caracteres manuscritos. As arquiteturas propostas neste trabalho foram escolhidas de acordo com dois tipos de aplicados de reconhecimento: reconhecimento de dígitos manuscritos isolados e reconhecimento automático de código postal. No estudo de casos, as RNAs foram modeladas para fazer o reconhecimento automático de código postal. Este estudo consistiu de um conjunto de implementações com o objetivo de testar o desempenho de um sistema OCR baseado em redes neurais. Foram feitos testes com dois tipos de sistemas de reconhecimento por redes neurais: redes totalmente conectadas e redes parcialmente. Para os dois casos foram utilizados amostras reais colhidas de 73 escritores. Os resultados obtidos com os dois tipos de redes foram comparados e comprovaram a superioridade das RNAs com arquitetura parcialmente conectada no reconhecimento de dígitos altamente ruidosos. Comparações também foram feitas com outras técnicas convencionais de reconhecimento, obtendo-se resultados, em muitos casos, superiores.
This dissertation investigates the use of Artificial Neural Networks (ANNs) for character recognition, especially handwritten digits. Real samples of isolated and postal code digits were used from different writers. The dissertation covers four main part: the study of methodologies, semantics and structure on character recognition and its representation; the development of the digits preprocessing phases; the design of ANNs to handwritten digits recognition; and the case studies. The first part of this dissertation studies methodologies, semantics and structures used on character recognition. The result of this study is an overview of the major aplication in OCR (Optical Character Recognition). Different kinds of semantics and their structures were classified according to each specific application. Several conventional models and intelligent systems, used in the classification stage of OCR systems, had also been discussed. The development of the digits preprocessing involved the investigation of different methodologies related to each preprocessing phase. The most used algorithm for each preprocessing phase were considered: thresholding, smoothing, segmentation and normalization. According to this study, specific algorithms were selected and developed. In the design of ANNs for handwritten digits recognition, different methodologies had been investigated, including the architetures and the learning algorithms most used. This overview confirmed the predominance of BackPropagation as the training algorithm for the Neural Network in this application. The architetures proposed in this work had been selected according to two types of applications of character recognition: isolated handwritten digits recognition and postal address code recognition. The case studies consisted of the designing of an ANN to postal address code recognition. The case studies involved testing the system performance for two kinds of ANNs: fully connected networks and partially connected networks. In both cases, samples of 73 writers were used. The results were compared to each other, confirming the superiority of partially connected ANN in handling noisy digits. The ANN perfomance was also compared with the perfomance of other conventional techniques, achieving better results in many cases.
Dahlstedt, Olle. "Automatic Handwritten Text Detection and Classification". Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-453809.
Texto completoSiddiqi, Imran-Ahmed. "Classification of handwritten documents : writer recognition". Paris 5, 2009. http://www.theses.fr/2009PA05S013.
Texto completoThe problem of identifying the writer of a handwritten document image has been an active research area over the last few years and enjoys applications in forensic and historical document analysis. We have developed an effective method for automatic writer identification and verification from unconstrained handwritten text images. Our method relies on two different aspects of writing: the presence of redundant patterns in the writing and its visual attributes. Based on the hypothesis that handwriting carries certain patterns that an individual would use frequently as he writes, we look to extract these patterns by analyzing small writing fragments and grouping similar patterns into clusters. In fact this corresponds more to the redundancy of writing gestures than writing shapes. These clusters are determined either for each of the writers separately or, for a group of writers generating a universal set of patterns. The writing in question is then compared to the produced clusters. We next exploit two important visual attributes of writing, the orientation and curvature, which enable to distinguish one writing from another. These attributes are extracted by computing a set of features from writing samples at different levels of observation. Two writings are then compared by computing distances between their respective features. Finally, we combine the two facets of handwriting to characterize the writer of a handwritten sample. The proposed methodology, evaluated on modern as well as ancient writings exhibited promising results on tasks of writer recognition and handwriting classification
Giménez, Pastor Adrián. "Bernoulli HMMs for Handwritten Text Recognition". Doctoral thesis, Universitat Politècnica de València, 2014. http://hdl.handle.net/10251/37978.
Texto completoGiménez Pastor, A. (2014). Bernoulli HMMs for Handwritten Text Recognition [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37978
TESIS
Fernández, Mota David. "Contextual word spotting in historical handwritten documents". Doctoral thesis, Universitat Autònoma de Barcelona, 2014. http://hdl.handle.net/10803/309292.
Texto completoThere are countless collections of historical documents in archives and libraries that contain plenty of valuable information for historians and researchers. The extraction of this information has become a central task among the Document Analysis researches and practitioners. There is an increasing interest to digital preserve and provide access to these kind of documents. But only the digitalization is not enough for the researchers. The extraction and/or indexation of information of this documents has had an increased interest among researchers. In many cases, and in particular in historical manuscripts, the full transcription of these documents is extremely di cult due the inherent de ciencies: poor physical preservation, di erent writing styles, obsolete languages, etc. Word spotting has become a popular an e cient alternative to full transcription. It inherently involves a high level of degradation in the images. The search of words is holistically formulated as a visual search of a given query shape in a larger image, instead of recognising the input text and searching the query word with an ascii string comparison. But the performance of classical word spotting approaches depend on the degradation level of the images being unacceptable in many cases . In this thesis we have proposed a novel paradigm called contextual word spotting method that uses the contextual/semantic information to achieve acceptable results whereas classical word spotting does not reach. The contextual word spotting framework proposed in this thesis is a segmentation-based word spotting approach, so an e cient word segmentation is needed. Historical handwritten documents present some common di culties that can increase the di culties the extraction of the words. We have proposed a line segmentation approach that formulates the problem as nding the central part path in the area between two consecutive lines. This is solved as a graph traversal problem. A path nding algorithm is used to nd the optimal path in a graph, previously computed, between the text lines. Once the text lines are extracted, words are localized inside the text lines using a word segmentation technique from the state of the art. Classical word spotting approaches can be improved using the contextual information of the documents. We have introduced a new framework, oriented to handwritten documents that present a highly structure, to extract information making use of context. The framework is an e cient tool for semi-automatic transcription that uses the contextual information to achieve better results than classical word spotting approaches. The contextual information is automatically discovered by recognizing repetitive structures and categorizing all the words according to semantic classes. The most frequent words in each semantic cluster are extracted and the same text is used to transcribe all them. The experimental results achieved in this thesis outperform classical word spotting approaches demonstrating the suitability of the proposed ensemble architecture for spotting words in historical handwritten documents using contextual information.
Zhou, Jie. "Recognition and verification of unconstructed handwritten numerals". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0018/NQ47716.pdf.
Texto completoChai, Sin-Kuo. "Multiclassifier neural networks for handwritten character recognition". Ohio : Ohio University, 1995. http://www.ohiolink.edu/etd/view.cgi?ohiou1174331633.
Texto completoVemulapalli, Smita. "Audio-video based handwritten mathematical content recognition". Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45958.
Texto completoKoerich, Alessandro L. "Large vocabulary off-line handwritten word recognition". Mémoire, École de technologie supérieure, 2002. http://espace.etsmtl.ca/818/1/KOERICH_Alessandro_L..pdf.
Texto completoClarke, Eddie. "A novel approach to handwritten character recognition". Thesis, University of Nottingham, 1995. http://eprints.nottingham.ac.uk/14035/.
Texto completoAbuhaiba, Ibrahim S. I. "Recognition of off-line handwritten cursive text". Thesis, Loughborough University, 1996. https://dspace.lboro.ac.uk/2134/7331.
Texto completoAl-Ma'adeed, Somaya A. S. "Recognition of off-line handwritten Arabic words". Thesis, University of Nottingham, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.403960.
Texto completoHendrawan. "Recognition and verification of handwritten postal addresses". Thesis, University of Essex, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.241209.
Texto completoWang, Jonathan M. Eng Massachusetts Institute of Technology. "Pentimento : non-sequential authoring of handwritten lectures". Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100619.
Texto completoThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Pentimento is software developed under the supervision of Fredo Durand in the Computer Graphics Group at CSAIL that focuses on dramatically simplifying the creation of online educational video lectures such as those of Khan Academy. In these videos, the lecture style is that the educator draws on a virtual whiteboard as he/she speaks. Currently, the type of software that the educator uses is very rudimentary in its functionality and only allows for basic functionality such as screen and voice recording. A downside of this approach is that the educator must get it right on the first approach, as there is no ability to simply edit the content taken during a screen capture after the initial recording without using unnecessarily complex video editing software. Even with video editing software, the user is not able to access the original drawing content used to create video. The overall goal of this project is to develop lecture recording software that uses a vector based representation to keep track of the user's sketching, which will allow the user to easily editing the original drawing content retroactively. The goal for my contribution to this project is to implement components for a web-based version of Pentimento. This will allow the application to reach a broader range of users. The goal is to have an HTML5 and Javascript based application that can run on many of popular the web browsers in use today. One of my main focuses in this project is to work on the audio recording and editing component. This includes the working on the user interface component and integrating it with the rest of the parts in the software.
by Jonathan Wang.
M. Eng.
Von, Tish Kelsey Leigh. "Interpretation and clustering of handwritten student responses". Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/77003.
Texto completoCataloged from PDF version of thesis.
Includes bibliographical references (p. 81-82).
This thesis presents an interpretation and clustering framework for handwritten student responses on tablet computers. The ink analysis system is able to capture and interpret digital ink strokes for many types of classroom exercises, including graphs, number lines, and fraction shading problems. By approaching the problem with both online and offline ink interpretation methods, relevant information is extracted from sets of ink strokes to produce a representation of a student's answer. A clustering algorithm is then used to group similar student responses. Overall, this approach makes it easier for teachers to view a set of responses and subsequently supply feedback to his or her students.
by Kelsey Leigh Von Tish.
M.Eng.
Sindle, Colin. "Handwritten signature verification using hidden Markov models". Thesis, Stellenbosch : Stellenbosch University, 2003. http://hdl.handle.net/10019.1/53445.
Texto completoENGLISH ABSTRACT: Handwritten signatures are provided extensively to verify identity for all types of transactions and documents. However, they are very rarely actually verified. This is because of the high cost of training and employing enough human operators (who are still fallible) to cope with the demand. They are a very well known, yet under-utilised biometric currently performing far below their potential. We present an on-line/dynamic handwritten signature verification system based on Hidden Markov Models, that far out performs human operators in both accuracy and speed. It uses only the local signature features-sampled from an electronic writing tablet-after some novel preprocessing steps, and is a fully automated system in that there are no parameters that need to be manually fine-tuned for different users. Novel verifiers are investigated which attain best equal error rates of between 2% and 5% for different types of high quality deliberate forgeries, and take a fraction of a second to accept or reject an identity claim on a 700 MHz computer.
AFRIKAANSE OPSOMMING: Geskrewe handtekeninge word gereeld gebruik om die identiteit van dokumente en transaksies te bevestig. Aangesien dit duur is in terme van menslike hulpbronne, word die integrit eit daarvan selde nagegaan. Om handtekeninge deur menslike operateurs te verifieër. is ook feilbaar-lOO% akkurate identifikasie is onrealisties. Handtekeninge is uiters akkurate en unieke identifikasie patrone wat in die praktyk nie naastenby tot hul volle potensiaal gebruik word nie. In hierdie navorsing gebruik ons verskuilde Markov modelle om dinamiese handtekeningherkenningstelsels te ontwikkel wat, in terme van spoed en akkuraatheid heelwat meer effektief as operateurs is. Die stelsel maak gebruik van slegs lokale handtekening eienskappe (en verwerkings daarvan) soos wat dit verkry word vanaf 'n elektroniese skryftablet. Die stelsel is ten volle outomaties en geen parameters hoef aangepas te word vir verskillende gebruikers nie. 'n Paar tipes nuwe handtekeningverifieërders word ondersoek en die resulterende gelykbreekpunt vir vals-aanvaardings- en vals-verwerpingsfoute lê tussen 2% en 5% vir verskillende tipes hoë kwaliteit vervalsde handtekeninge. Op 'n tipiese 700 MHz verwerker word die identiteit van 'n persoon ill minder as i sekonde bevestig.
Zhang, Ting. "New Architectures for Handwritten Mathematical Expressions Recognition". Thesis, Nantes, 2017. http://www.theses.fr/2017NANT4054/document.
Texto completoAs an appealing topic in pattern recognition, handwritten mathematical expression recognition exhibits a big research challenge and underpins many practical applications. Both a large set of symbols (more than 100) and 2-D structures increase the difficulty of this recognition problem. In this thesis, we focus on online handwritten mathematical expression recognition using BLSTM and CTC topology, and finally build a graph-driven recognition system, bypassing the high time complexity and manual work in the classical grammar-driven systems. To allow the 2-D structured language to be handled by the sequence classifier, we extend the chain-structured BLSTM to an original Tree-based BLSTM, which could label a tree structured data. The CTC layer is adapted with local constraints, to align the outputs and at the same time benefit from introducing the additional ’blank’ class. The proposed system addresses the recognition task as a graph building problem. The input expression is a sequence of strokes, and then an intermediate graph is derived considering temporal and spatial relations among strokes. Next, several trees are derived from the graph and labeled with Tree-based BLSTM. The last step is to merge these labeled trees to build an admissible stroke label graph (SLG) modeling 2-D formulas uniquely. One major difference with the traditional approaches is that there is no explicit segmentation, recognition and layout extraction steps but a unique trainable system that produces directly a SLG describing a mathematical expression. The proposed system, without any grammar, achieves competitive results in online math expression recognition domain
Romero, Gómez Verónica. "Multimodal Interactive Transcription of Handwritten Text Images". Doctoral thesis, Universitat Politècnica de València, 2010. http://hdl.handle.net/10251/8541.
Texto completoRomero Gómez, V. (2010). Multimodal Interactive Transcription of Handwritten Text Images [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8541
Palancia
Nina, Oliver. "Text Segmentation of Historical Degraded Handwritten Documents". BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2585.
Texto completoKaplani, Eleni. "Human and computer-based verification of handwritten signatures". Thesis, University of Kent, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.396378.
Texto completoAl-Emami, Samir Yaseen Safa. "Machine recognition of handwritten and typewritten Arabic characters". Thesis, University of Reading, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359173.
Texto completoLeedham, C. G. "Computer acquisition and recognition of Pitman's handwritten shorthand". Thesis, University of Southampton, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.355330.
Texto completoAbayan, Marlon 1974. "A system for offline cursive handwritten word recognition". Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/42731.
Texto completoLe, Riche Pierre (Pierre Jacques). "Handwritten signature verification : a hidden Markov model approach". Thesis, Stellenbosch : Stellenbosch University, 2000. http://hdl.handle.net/10019.1/51784.
Texto completoENGLISH ABSTRACT: Handwritten signature verification (HSV) is the process through which handwritten signatures are analysed in an attempt to determine whether the person who made the signature is who he claims to be. Banks and other financial institutions lose billions of rands annually to cheque fraud and other crimes that are preventable with the aid of good signature verification techniques. Unfortunately, the volume of cheques that are processed precludes a thorough HSV process done in the traditional manner by human operators. It is the aim of this research to investigate new methods to compare signatures automatically, to eventually speed up the HSV process and improve on the accuracy of existing systems. The new technology that is investigated is the use of the so-called hidden Markov models (HMMs). It is only quite recently that the computing power has become commonly available to make the real-time use of HMMs in pattern recognition a possibility. Two demonstration programs, SigGrab and Securitlheque, have been developed that make use of this technology, and show excellent improvements over other techniques and competing products. HSV accuracies in excess of99% can be attained.
AFRIKAANSE OPSOMMING: Handgeskrewe handtekening verifikasie (HHV) is die proses waardeur handgeskrewe handtekeninge ondersoek word in 'n poging om te bevestig of die persoon wat die handtekening gemaak het werklik is wie hy voorgee om te wees. Banke en ander finansiele instansies verloor jaarliks biljoene rande aan tjekbedrog en ander misdrywe wat voorkom sou kon word indien goeie metodes van handtekening verifikasie daargestel kon word. Ongelukkig is die volume van tjeks wat hanteer word so groot, dat tradisionele HHV deur menslike operateurs 'n onbegonne taak is. Dit is die doel van hierdie navorsmg om nuwe metodes te ondersoek om handtekeninge outomaties te kan vergelyk en so die HHV proses te bespoedig en ook te verbeter op die akkuraatheid van bestaande stelsels. Die nuwe tegnologie wat ondersoek is is die gebruik van die sogenaamde verskuilde Markov modelle (VMMs). Dit is eers redelik onlangs dat die rekenaar verwerkingskrag algemeen beskikbaar geraak het om die intydse gebruik van VMMs in patroonherkenning prakties moontlik te maak. Twee demonstrasieprogramme, SigGrab en SecuriCheque, is ontwikkel wat gebruik maak van hierdie tegnologie en toon uitstekende verbeterings teenoor ander tegnieke en kompeterende produkte. 'n Akkuraatheid van 99% of hoer word tipies verkry.
Solimanpour, Farshid. "Farsi handwritten databases and offline handwritten isolated digits recognition". Thesis, 2007. http://spectrum.library.concordia.ca/975259/1/MR28955.pdf.
Texto completoCai, Hechun. "Handwritten digits recognition". 1991. http://catalog.hathitrust.org/api/volumes/oclc/25257714.html.
Texto completoTypescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaf 22).
Wang, Chih-Jian y 王志堅. "Handwritten Chinese Radical Recognition". Thesis, 1996. http://ndltd.ncl.edu.tw/handle/43638470887196170782.
Texto completo國立成功大學
工程科學系
84
Owing to large amount of Chinese character set, the recognition of handwritten Chinese character is difficult. In order to overcome this problem, many kinds of preclassification schemes have been proposed. Because the Chinese character is composed of main radicals, subradicals and remaining strokes, the recognition methods based on radicals embedded in Chinese characters are intuitive and commonly used. In this paper we modify the 46 set of radicals defined in Ching- Song input method to satisfy the requirements of optical character recognition and introduce the structural information of radicals. After the preprocessing and segment extraction steps, the proposed dividing algorithm is used to separate the thinned character image into several independent substructures of strokes, according to the connectivity among strokes. Under the guidance of structural model, that is composed of 1-D relation string and structural information of radicals, the predefined stroke sequence is searched from the candidate strokes by a depth first searching method. Not only can the method we proposed above recognize the radical sets embedded in a character, but also accomplish the preclassification work at the same time. Our method has following advantages: (1)only a little amount of defined radical needed and less strokes for each radical; (2)the recognition speed is fast; (3)the structural information of radicals is salient and stable.
Lu, Chang-Sheng y 呂昶昇. "Handwritten Score Recording System". Thesis, 2002. http://ndltd.ncl.edu.tw/handle/38237896952501456012.
Texto completo大同大學
資訊工程研究所
90
In preparing the entrance examination, the registration number of examinee on the answer sheet should be sealed in advance. After the teacher graded the score on the answer sheet, the operator takes the seal away and records the score into the database. In order to reduce the processing time and cost, we propose a score recording system which uses the bar code to represent the registration number of examinee and recognizes and records the handwritten score automatically. In this thesis, we use an approach which extracts areas having high density of mono-oriented gradient to locate the position of bar code. We use multiple features to recognize the score, and offer a confidence index sound to inform the operator how the system rate the confidence of recognition. According to the confidence index, the operator can determine if confirmation for the score on the answer sheet is required. Experimental results show our system not only can locate bar code accurately, but also can achieve the demand on recognizing the score. Keywords: handwritten numeral recognition, bar code location
Bhargav, S. "Handwritten Devanagari numeral recognition". Thesis, 2014. http://ethesis.nitrkl.ac.in/6477/1/e-35.pdf.
Texto completoMs, Shalini. "Handwritten Hindi Character Recognition". Thesis, 2017. http://ethesis.nitrkl.ac.in/8879/1/2017_MT_Shalini.pdf.
Texto completoChen, Yueting. "Handwritten numeral recognition using multiwavelets". Thesis, 2002. http://spectrum.library.concordia.ca/1812/1/MQ72929.pdf.
Texto completoKuo, Kuei-Lan y 郭癸蘭. "Handwritten ID Number Recognition System". Thesis, 2002. http://ndltd.ncl.edu.tw/handle/28798089241718556602.
Texto completo國立高雄第一科技大學
電腦與通訊工程系
90
This thesis brings up the implementation of handwritten ID number recognition system by the application of plastic perceptron neural network(PPNN). The applied structure of PPNN in this thesis is improved from the learning algorithm and network structure of back-propagation neural network (BPNN)in artificial neural networks. The problems of traditional BPNN such as longer learning period, not prone to convergence, re-training while delete or add new patterns make the realization of real time BPNN system impossible. The proposed methods are combined with the parallel distributive process concept and modification of the BPNN structure could accelerate the learning speed and solve the re-training problem. The character segmentation, noise removal and extraction of feature are also discussed. Adequate extracted feature make recognition of character easier. The adoption of white run-length and pixel density could clearly display the structural and integral of the character respectively, and facilitate to make higher recognition accuracy.
MacLean, Scott. "Automated recognition of handwritten mathematics". Thesis, 2014. http://hdl.handle.net/10012/8328.
Texto completoGong, Shyh-Jier y 龔世傑. "Recognition of handwritten digit characters". Thesis, 1993. http://ndltd.ncl.edu.tw/handle/19034124518507636417.
Texto completo大同工學院
資訊工程研究所
81
This paper presents a methodology for classifying syntactic patterns is using a feature matching against a set of proto- otypes. The prototypes are first classified and arranged into a hierarchical structure that facilitates this matching. Image of characters are described by a sequence of features extracted from the chain codes of their contours. A rotatio- nally invariant string distance measure is defined that com- pared two feature strings. The methodology discussed in this paper is compared to a nearest neighbor classifier that use 2,010 prototypes. The proposed technique can get a recognit- ion rate of greater than 97 percent, and the recognition sp- eed is 0.5 sec/char.
Lo, Wei-Hsien y 羅尉賢. "Video-based Handwritten Signature Verification". Thesis, 2010. http://ndltd.ncl.edu.tw/handle/68968926979823245638.
Texto completo國立中央大學
資訊工程研究所
98
This paper proposes a video-based handwritten signature verification framework. When acquiring signature information, we use a webcam in substitution for a digitizing tablet. Because webcams are more prevalent and cheaper than digitizing tablets, using webcams as sensors can reduce the cost. In addition, the features extracted using a webcam also contain more information. In tradition handwritten signature verification, features extracted using a digitizing tablet are mainly trajectories. But for the features extracted using a webcam, we can acquire pen grasping posture information of the subscriber in addition to the trajectories of the signature. Therefore, in the proposed framework, we perform video-based handwritten signature verification using two different types of feature information. For the first type of feature, we perform curvelet transform on the subscriber’s writing trajectory to obtain static information. The second type of feature is dynamic information which is the pen grasping posture of the subscriber. The dynamic feature is represented by motion energy image (MEI). We cascade the classifiers using static information and dynamic information to perform handwritten signature verification. The proposed video-based handwritten signature verification framework achieves a low false acceptance rate of 0% and false rejection rate 0.5% for our handwritten signature database without imitation signatures. For the database with imitation signatures, the proposed framework can also achieve a low false acceptance rate of 0.05%.
李健宏. "Off-line Handwritten Numeral Recognition". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/53649071798915257751.
Texto completo國立臺灣師範大學
工業教育學系在職進修碩士班
91
Recognition of off-line handwritten numerals has been the subject of research for many years. Since handwritten numerals widely vary in their shapes, recognizing them has been difficult and challenging. Although a high level of recognition has been achieved, the shortcomings of time-consuming learning and recognition still persist. The present research focuses on overcoming these defects, while maintaining a high recognition level. The research discussed in the present paper makes use of the MNIST database for learning and testing. For feature extraction, statistic features are used in the present research. Employing statistic features is saddled with the difficulty of a high number of dimensions, yet the present research, by using 130 dimensions, is able to distinguish between ten classifications. To make character recognition more effective, in the present research transformation by Fisher''s LDF (linear discriminant function) is applied to input characters. As experiments have shown, after transformation of non-clustered features (without learning) a level of recognition of 92.6% is achieved. In the present research, the method of WGLVQ, which is based on GLVQ (generalized learning vector quantization), is employed. Better convergence is achieved by GLVQ, and it is able to improve for LVQ. Experiments conducted within the current research have shown that both LVQ and GLVQ, applied to recognizing handwritten numerals, have quite good convergence behavior, also confirming the effectiveness of feature processing presented here. In the present research, the methods of LVQ and GLVQ are enhanced by weighting, yielding novel methods of WLVQ and WGLVQ. Therein, in every learning step, not only directions classifying reference vectors are adjusted, but also weights of every vector. With every step the weights of less-weighted vectors decrease, resulting in more pronounced distinctions of light and heavy weights. According to experiments, both WLVQ and WGLVQ exhibit more effective character recognition. With classification by WGLVQ and including learning, in an open test a level of recognition of 97.6% is achieved. With 16 clusters for each class, the recognition level rises to 98.2%. This result trails the level of 99.3% attained by Ernst using classification by LIRA, but while recognizing 10000 samples takes 30 minutes for the LIRA’s classification, the present approach allows recognition of 10000 samples in 1 - 2 minutes. The present research offers a more practical approach.
"Content Detection in Handwritten Documents". Master's thesis, 2018. http://hdl.handle.net/2286/R.I.50452.
Texto completoDissertation/Thesis
Masters Thesis Computer Science 2018
Chen, Ching-Yi y 陳慶逸. "Off-line Handwritten Character Recognition". Thesis, 1998. http://ndltd.ncl.edu.tw/handle/78087161407572953101.
Texto completo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.
Sharma, Anand. "Devanagari Online Handwritten Character Recognition". Thesis, 2019. https://etd.iisc.ac.in/handle/2005/4633.
Texto completoGhabrial, Melad Y. "Parallel algorithms for handwritten character recognition". Thesis, 1990. http://spectrum.library.concordia.ca/3859/1/MM97702.pdf.
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