Thesis (M.S.S.I.S.)--Regis University, Denver, Colo., 2010. / Title from PDF title page (viewed on Jul. 14, 2010). Includes bibliographical references.

No description available.

No description available.

M.Com. (Informatics) / This dissertation examines the field of computer vision, with special attention given to the recognition of alpha numeric characters on video images using OCR software. The study may be broadly divided into four sections. The first section offers an introduction to standard OCR (Optical Character Recognition) methods that have evolved over the years and have been incorporated into some commercial software packages currently. The second section covers the problem of reading characters in a dynamic environment and also the problems experienced with the compatibility of current OCR software products. The third section of the dissertation looks at solutions for the problem mentioned in section two and creates a framework for a generic model in which any application should fit. The generic model is then described in detail. The framework should provide a foundation for interested parties to build, modify or improve the model. The final section gives examples of how the model should present a solution. Experimental results are looked at and the model is critically evaluated.

Thesis (M.S.)--Rochester Institute of Technology, 1993. / Typescript. Includes bibliographical references (leaves 138-139).

Thesis (Ph. D.)--University of Washington, 1999. / Vita. Includes bibliographical references (leaves 172-180).

Report (M.S.)--Virginia Polytechnic Institute and State University. M.S. 1993. / Abstract. Includes bibliographical references (leaf 50). Also available via the Internet.

Thesis (M.Eng.Sc.) - University of Queensland, 2004. / Includes bibliography.

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2008. / Submitted by Jaqueline Oliveira (jaqueoliveiram@gmail.com) on 2008-11-21T11:33:16Z No. of bitstreams: 1 DISSERTACAO_2008_FabioLucioLMendonca.pdf: 1577853 bytes, checksum: f9d0f561e4281eac3a1f4808f3e239ea (MD5) / Approved for entry into archive by Georgia Fernandes(georgia@bce.unb.br) on 2009-02-06T10:39:13Z (GMT) No. of bitstreams: 1 DISSERTACAO_2008_FabioLucioLMendonca.pdf: 1577853 bytes, checksum: f9d0f561e4281eac3a1f4808f3e239ea (MD5) / Made available in DSpace on 2009-02-06T10:39:13Z (GMT). No. of bitstreams: 1 DISSERTACAO_2008_FabioLucioLMendonca.pdf: 1577853 bytes, checksum: f9d0f561e4281eac3a1f4808f3e239ea (MD5) / Este trabalho objetiva propor uma arquitetura de um sistema para tratamento e reconhecimento automático do texto de documentos paleográficos, utilizando um OCR (Optical Character Recognition) com tecnologia de redes neurais artificiais. O sistema proposto deve atuar no contexto de processos de transcrição do texto de documentos de escritas paleográficas do século XVI ao XIX, documentos estes do Brasil colônia que foram digitalizados a partir dos originais impressos arquivados no Arquivo Ultramarino de Lisboa, uma das realizações do Projeto Resgate do Ministério da Cultura brasileiro. A arquitetura do sistema proposto inclui módulos para segmentar as imagens digitalizadas dos documentos, para análise dos segmentos com OCR na tentativa de reconhecimento do texto, para treinamento do OCR com formação de um dicionário de palavras reconhecidas e para armazenamento do texto transcrito a partir das imagens dos documentos. Para avaliar essa arquitetura foi desenvolvido um protótipo de software que permite ao usuário segmentar manualmente uma imagem de documento, treinar um OCR simples e extrair com esse OCR algumas informações de texto do documento paleográfico digitalizado. Conclui-se que a arquitetura proposta é funcional, ainda que sejam necessários desenvolvimentos mais profundos no que se refere aos processos de segmentação dos documentos e reconhecimento das escritas paleográficas do século XVI ao XIX. ___________________________________________________________________________________________ ABSTRACT / This work propose a system architecture for automatic manipulate and recognize of text on paleographic document, using Optical Character Recognition (OCR) aggregate with artificial neural networks. The system should work on the context of process text transcription on text documents with paleographic writing of century XVI to XIX; those documents are acquired from Brazil on colony age and digitalized from the original files archived on Ultramario Archive of Lisboa, one works of Projeto Resgate from Brazilian Culture Ministry. The architecture of propose system has modules for segment the digital image of documents, analyze of segments with OCR in try of text recognize, OCR training for compose a dictionary of recognized worlds and also a module for storage the transcript text from document images. For evaluation has been developed prototype software, where one user could manually segment a document image, simple OCR training and using this OCR gets some text information from a digital paleographic document. We conclude that the propose architecture was functional, but still need more improvements on document segmentation module and on module that recognize the paleographic writings of century XVI to XIX.

This study proposes a generic model for optical character recognition (OCR) on remote objects, using digitised video and digital camera input. Currently, most applications using this technique are ad hoc. Lack of formalism creates the risk of fragmented adhocracy. The purpose of this model is to introduce some degree of formalism based on classical character recognition, thereby providing a theoretical basis, and to explore the potential of character recognition on digital images for automated reading. The model also propogates the use of off the shelf commercial software and hardware currently available. The model “CRODI” which is an abbreviation for Character Recognition On Digital Images, consists of several objects or building blocks necessary for any remote character recognition application. In order to provide an optimal solution for any application it is necessary to select the most appropriate and effective building blocks. The combination and sequence in which these building blocks are selected is called a visual routine. This study does not only focus on choosing the correct building blocks for an application but also describes how certain visual routines and methodologies can be used to cater for applications with a certain profile. Furthermore, a formal notation in the form of an Expert Support System is introduced to describe optimised visual routines and the selection of building blocks in an efficient way. An example is given in chapter six of the efficiency and effectiveness of the model on a classical but dynamic application, where character recognition is performed on stationary and fast moving vehicles’ number plates. The biggest advantage of CRODI is to propose a generic framework or model from which an intuitive selection of objects, visual routines or methodologies can occur to expedite and optimise remote character recognition on almost any stationary or moving object. / Ehlers, E.M., Prof.