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Proposta de arquitetura de um sistema com base em OCR neuronal para resgate e indexação de escritas paleográficas do sec. XVI ao XIX; Proposal of an system architeture based on neural OCR for rescue and index paleography writens between XVI and XIX centuries

Mendonça, Fábio Lúcio Lopes de
Fonte: Universidade de Brasília Publicador: Universidade de Brasília
Tipo: Dissertação
POR
Relevância na Pesquisa
66.11%
Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2008.; 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...

Rapid Feature Extraction for Optical Character Recognition

Hossain, M. Zahid; Amin, M. Ashraful; Yan, Hong
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 01/06/2012
Relevância na Pesquisa
66.05%
Feature extraction is one of the fundamental problems of character recognition. The performance of character recognition system is depends on proper feature extraction and correct classifier selection. In this article, a rapid feature extraction method is proposed and named as Celled Projection (CP) that compute the projection of each section formed through partitioning an image. The recognition performance of the proposed method is compared with other widely used feature extraction methods that are intensively studied for many different scripts in literature. The experiments have been conducted using Bangla handwritten numerals along with three different well known classifiers which demonstrate comparable results including 94.12% recognition accuracy using celled projection.; Comment: 5 pages, 1 figure

Boosting Optical Character Recognition: A Super-Resolution Approach

Dong, Chao; Zhu, Ximei; Deng, Yubin; Loy, Chen Change; Qiao, Yu
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 06/06/2015
Relevância na Pesquisa
76.01%
Text image super-resolution is a challenging yet open research problem in the computer vision community. In particular, low-resolution images hamper the performance of typical optical character recognition (OCR) systems. In this article, we summarize our entry to the ICDAR2015 Competition on Text Image Super-Resolution. Experiments are based on the provided ICDAR2015 TextSR dataset and the released Tesseract-OCR 3.02 system. We report that our winning entry of text image super-resolution framework has largely improved the OCR performance with low-resolution images used as input, reaching an OCR accuracy score of 77.19%, which is comparable with that of using the original high-resolution images 78.80%.; Comment: 5 pages, 8 figures

Artificial Neural Network Based Optical Character Recognition

Shrivastava, Vivek; Sharma, Navdeep
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 19/11/2012
Relevância na Pesquisa
76.14%
Optical Character Recognition deals in recognition and classification of characters from an image. For the recognition to be accurate, certain topological and geometrical properties are calculated, based on which a character is classified and recognized. Also, the Human psychology perceives characters by its overall shape and features such as strokes, curves, protrusions, enclosures etc. These properties, also called Features are extracted from the image by means of spatial pixel-based calculation. A collection of such features, called Vectors, help in defining a character uniquely, by means of an Artificial Neural Network that uses these Feature Vectors.; Comment: Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.5, October 2012

Handwritten Bangla Basic and Compound character recognition using MLP and SVM classifier

Das, Nibaran; Das, Bindaban; Sarkar, Ram; Basu, Subhadip; Kundu, Mahantapas; Nasipuri, Mita
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
66.1%
A novel approach for recognition of handwritten compound Bangla characters, along with the Basic characters of Bangla alphabet, is presented here. Compared to English like Roman script, one of the major stumbling blocks in Optical Character Recognition (OCR) of handwritten Bangla script is the large number of complex shaped character classes of Bangla alphabet. In addition to 50 basic character classes, there are nearly 160 complex shaped compound character classes in Bangla alphabet. Dealing with such a large varieties of handwritten characters with a suitably designed feature set is a challenging problem. Uncertainty and imprecision are inherent in handwritten script. Moreover, such a large varieties of complex shaped characters, some of which have close resemblance, makes the problem of OCR of handwritten Bangla characters more difficult. Considering the complexity of the problem, the present approach makes an attempt to identify compound character classes from most frequently to less frequently occurred ones, i.e., in order of importance. This is to develop a frame work for incrementally increasing the number of learned classes of compound characters from more frequently occurred ones to less frequently occurred ones along with Basic characters. On experimentation...

Sequence to Sequence Learning for Optical Character Recognition

Sahu, Devendra Kumar; Sukhwani, Mohak
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 13/11/2015
Relevância na Pesquisa
76.01%
We propose an end-to-end recurrent encoder-decoder based sequence learning approach for printed text Optical Character Recognition (OCR). In contrast to present day existing state-of-art OCR solution which uses connectionist temporal classification (CTC) output layer, our approach makes minimalistic assumptions on the structure and length of the sequence. We use a two step encoder-decoder approach -- (a) A recurrent encoder reads a variable length printed text word image and encodes it to a fixed dimensional embedding. (b) This fixed dimensional embedding is subsequently comprehended by decoder structure which converts it into a variable length text output. Our architecture gives competitive performance relative to connectionist temporal classification (CTC) output layer while being executed in more natural settings. The learnt deep word image embedding from encoder can be used for printed text based retrieval systems. The expressive fixed dimensional embedding for any variable length input expedites the task of retrieval and makes it more efficient which is not possible with other recurrent neural network architectures. We empirically investigate the expressiveness and the learnability of long short term memory (LSTMs) in the sequence to sequence learning regime by training our network for prediction tasks in segmentation free printed text OCR. The utility of the proposed architecture for printed text is demonstrated by quantitative and qualitative evaluation of two tasks -- word prediction and retrieval.; Comment: 9 pages (including reference)...

Kannada Character Recognition System A Review

Indira, K.; Selvi, S. Sethu
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 29/01/2010
Relevância na Pesquisa
66.08%
Intensive research has been done on optical character recognition ocr and a large number of articles have been published on this topic during the last few decades. Many commercial OCR systems are now available in the market, but most of these systems work for Roman, Chinese, Japanese and Arabic characters. There are no sufficient number of works on Indian language character recognition especially Kannada script among 12 major scripts in India. This paper presents a review of existing work on printed Kannada script and their results. The characteristics of Kannada script and Kannada Character Recognition System kcr are discussed in detail. Finally fusion at the classifier level is proposed to increase the recognition accuracy.; Comment: 12 pages, 8 figures

Offline Handwritten MODI Character Recognition Using HU, Zernike Moments and Zoning

Kulkarni, Sadanand A.; Borde, Prashant L.; Manza, Ramesh R.; Yannawar, Pravin L.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
66.13%
HOCR is abbreviated as Handwritten Optical Character Recognition. HOCR is a process of recognition of different handwritten characters from a digital image of documents. Handwritten automatic character recognition has attracted many researchers all over the world to contribute handwritten character recognition domain. Shape identification and feature extraction is very important part of any character recognition system and success of method is highly dependent on selection of features. However feature extraction is the most important step in defining the shape of the character as precisely and as uniquely as possible. This is indeed the most important step and complex task as well and achieved success by using invariance property, irrespective of position and orientation. Zernike moments describes shape, identify rotation invariant due to its Orthogonality property. MODI is an ancient script of India had cursive and complex representation of characters. The work described in this paper presents efficiency of Zernike moments over Hu 7 moment with zoning for automatic recognition of handwritten MODI characters. Offline approach is used in this paper because MODI Script was very popular and widely used for writing purpose till 19th century before Devanagari was officially adopted.; Comment: This paper has been withdrawn by the author due to the paper was rejected by journal with a reson "paper was not suitable for the journal"

A survey of modern optical character recognition techniques

Borovikov, Eugene
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 12/12/2014
Relevância na Pesquisa
76.06%
This report explores the latest advances in the field of digital document recognition. With the focus on printed document imagery, we discuss the major developments in optical character recognition (OCR) and document image enhancement/restoration in application to Latin and non-Latin scripts. In addition, we review and discuss the available technologies for hand-written document recognition. In this report, we also provide some company-accumulated benchmark results on available OCR engines.; Comment: Technical report surveying OCR/ICR and document understanding methods as of 2004.It contains 38 pages, numerous figures, 93 references, and provides a table of contents

Design of an Optical Character Recognition System for Camera-based Handheld Devices

Mollah, Ayatullah Faruk; Majumder, Nabamita; Basu, Subhadip; Nasipuri, Mita
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 15/09/2011
Relevância na Pesquisa
76.07%
This paper presents a complete Optical Character Recognition (OCR) system for camera captured image/graphics embedded textual documents for handheld devices. At first, text regions are extracted and skew corrected. Then, these regions are binarized and segmented into lines and characters. Characters are passed into the recognition module. Experimenting with a set of 100 business card images, captured by cell phone camera, we have achieved a maximum recognition accuracy of 92.74%. Compared to Tesseract, an open source desktop-based powerful OCR engine, present recognition accuracy is worth contributing. Moreover, the developed technique is computationally efficient and consumes low memory so as to be applicable on handheld devices.

Optical Character Recognition, Using K-Nearest Neighbors

Wang, Wei
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 05/11/2014
Relevância na Pesquisa
76.01%
The problem of optical character recognition, OCR, has been widely discussed in the literature. Having a hand-written text, the program aims at recognizing the text. Even though there are several approaches to this issue, it is still an open problem. In this paper we would like to propose an approach that uses K-nearest neighbors algorithm, and has the accuracy of more than 90%. The training and run time is also very short.

Examples of Artificial Perceptions in Optical Character Recognition and Iris Recognition

Noaica, Cristina M.; Badea, Robert; Motoc, Iulia M.; Ghica, Claudiu G.; Rosoiu, Alin C.; Popescu-Bodorin, Nicolaie
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 27/09/2012
Relevância na Pesquisa
66.04%
This paper assumes the hypothesis that human learning is perception based, and consequently, the learning process and perceptions should not be represented and investigated independently or modeled in different simulation spaces. In order to keep the analogy between the artificial and human learning, the former is assumed here as being based on the artificial perception. Hence, instead of choosing to apply or develop a Computational Theory of (human) Perceptions, we choose to mirror the human perceptions in a numeric (computational) space as artificial perceptions and to analyze the interdependence between artificial learning and artificial perception in the same numeric space, using one of the simplest tools of Artificial Intelligence and Soft Computing, namely the perceptrons. As practical applications, we choose to work around two examples: Optical Character Recognition and Iris Recognition. In both cases a simple Turing test shows that artificial perceptions of the difference between two characters and between two irides are fuzzy, whereas the corresponding human perceptions are, in fact, crisp.; Comment: 5th Int. Conf. on Soft Computing and Applications (Szeged, HU), 22-24 Aug 2012

Context sensitive optical character recognition using neural networks and hidden Markov models

Elliott, Steven C.
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
EN_US
Relevância na Pesquisa
86.27%
This thesis investigates a method for using contextual information in text recognition. This is based on the premise that, while reading, humans recognize words with missing or garbled characters by examining the surrounding characters and then selecting the appropriate character. The correct character is chosen based on an inherent knowledge of the language and spelling techniques. We can then model this statistically. The approach taken by this Thesis is to combine feature extraction techniques, Neural Networks and Hidden Markov Modeling. This method of character recognition involves a three step process: pixel image preprocessing, neural network classification and context interpretation. Pixel image preprocessing applies a feature extraction algorithm to original bit mapped images, which produces a feature vector for the original images which are input into a neural network. The neural network performs the initial classification of the characters by producing ten weights, one for each character. The magnitude of the weight is translated into the confidence the network has in each of the choices. The greater the magnitude and separation, the more confident the neural network is of a given choice. The output of the neural network is the input for a context interpreter. The context interpreter uses Hidden Markov Modeling (HMM) techniques to determine the most probable classification for all characters based on the characters that precede that character and character pair statistics. The HMMs are built using an a priori knowledge of the language: a statistical description of the probabilities of digrams. Experimentation and verification of this method combines the development and use of a preprocessor program...

Optical character categorization: Clustering as it applies to OCR

Greenwald, Jennifer
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
EN_US
Relevância na Pesquisa
86.13%
I applied clustering analysis to the problem of creating tagged training data for optical character recognition (OCR). The creation of labeled character data by hand is a slow and cumbersome process. My belief is that clustering methods can be applied to character data before tagging it, allowing the operator to label entire groups of characters at once and greatly speeding the time in which tagged character data can be generated. This thesis will provide proof of concept as a basis for more in depth research and eventually the creation of a sophisticated application utilizing these techniques for the generation of labeled training data for OCR systems.

Advanced correlation-based character recognition applied to the Archimedes Palimpsest

Walvoord, Derek J.
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Dissertação
EN_US
Relevância na Pesquisa
66.13%
The Archimedes Palimpsest is a manuscript containing the partial text of seven treatises by Archimedes that were copied onto parchment and bound in the tenth-century AD. This work is aimed at providing tools that allow scholars of ancient Greek mathematics to retrieve as much information as possible from images of the remaining degraded text. Acorrelation pattern recognition (CPR) system has been developed to recognize distorted versions of Greek characters in problematic regions of the palimpsest imagery, which have been obscured by damage from mold and fire, overtext, and natural aging. Feature vectors for each class of characters are constructed using a series of spatial correlation algorithms and corresponding performance metrics. Principal components analysis (PCA) is employed prior to classification to remove features corresponding to filtering schemes that performed poorly for the spatial characteristics of the selected region-of-interest. A probability is then assigned to each class, forming a character probability distribution based on relative distances from the class feature vectors to the ROI feature vector in principal component (PC) space. However, the current CPR system does not produce a single classification decision...

The Application of neural networks to character recognition based on primitive feature detection

Pistacchio, Michael
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
EN_US
Relevância na Pesquisa
76.22%
This thesis investigates a character recognition method inspired by the premise that humans recognize shapes using their ability to assimilate a set of primitive features. These features collectively create a higher level shape of a certain category. The primitive features employed in our method include horizontal, vertical, diagonal lines, and corners of various orientations positioned at various places within a character. Combinations of these features form categories of characters to be recognized. The basic approach consists of preprocessing a character bitmap, extracting primitive features to form a feature vector. The feature vector is then input to a classification neural net. Based on weights derived during training, the system selects the character most closely identified by the feature vector. The advantages of this approach are the speed of training and recognition (as opposed to methods which continually iterate to the final solution), and robustness of the "blurring" effect realized by transforming a character bitmap to an array of features, rather than attempting template matching at the bitmap or pixel level. To support this study, a graphics workstation based environment has been developed, equiped with 3000 16X16 pixel characters...

Fuzzy approach for Arabic character recognition

El-Nasan, Adnan
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
Relevância na Pesquisa
66.06%
Pattern recognition/classification is increasingly drawing the attention of scientific research because of its important roll in automation and human-machine communication. Even though many models have been introduced to deal with classification, because of the inherited imprecision and ambiguity, these models did not tackle the problem in an efficient way. Traditional models deal only with statistical uncertainty (randomness) but not with the non-statistical uncertainty (vagueness). Fuzzy set theory allows us to better understand imprecision in both of its categories: vagueness and randomness. The incorporation of fuzzy set theory in existing algorithms helped in many cases to improve the performance and increase the efficiency of those algorithms. This thesis will explore fuzzy logic as it pertains to pattern recognition. In order to demonstrate fuzzy logic, the problem of recognizing the Arabic alphabet is discussed. In this problem moments and central moments were used as discriminating features. A fuzzy classifier was designed in a way that incorporated some statistical knowledge of the problem in hand. Performance of this classifier was compared to a Bayesian classifier and a neural network classifier. Performance, evaluation...

Document understanding system using stochastic context-free grammars

Handley, John; Namboodiri, Anoop; Zanibbi, Richard
Fonte: IEEE Computer Society : Eighth International Conference on Document Analysis and Recognition Publicador: IEEE Computer Society : Eighth International Conference on Document Analysis and Recognition
Tipo: Proceedings
EN_US
Relevância na Pesquisa
76.06%
We present a document understanding system in which the arrangement of lines of text and block separators within a document are modeled by stochastic context free grammars. A grammar corresponds to a document genre; our system may be adapted to a new genre simply by replacing the input grammar. The system incorporates an optical character recognition system that outputs characters, their positions and font sizes. These features are combined to form a document representation of lines of text and separators. Lines of text are labeled as tokens using regular expression matching. The maximum likelihood parse of this stream of tokens and separators yields a functional labeling of the document lines. We describe business card and business letter applications.; "The Recognition Strategy Language," Eighth International Conference on Document Analysis and Recognition. Held at Seoul, South Korea: 29 August - 1 September 2005. ©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Character recognition of optically blurred textual images using moment invariants

Hanson, Adam
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
EN_US
Relevância na Pesquisa
66.15%
Statistical moment invariants were used to generate a feature space for classifying images of text characters. The feature vector of a given letter is invariant to changes in scale, position, rotation, and contrast in the image. Test character images were generated by simulated optical blurring. Images were classified by calculating the distance between the feature vector of a given test character and that of each reference character. The test character was identified as the reference character for which the distance between feature vectors is a minimum. Significantly blurred characters were classified correctly using this method.

Optical Character Recognition Based Speech Synthesis System Using LabVIEW

Singla,S. K.; Yadav,R.K.
Fonte: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico Publicador: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/01/2014 EN
Relevância na Pesquisa
96.13%
Knowledge extraction by just listening to sounds is a distinctive property. Speech signal is more effective means of communication than text because blind and visually impaired persons can also respond to sounds. This paper aims to develop a cost effective, and user friendly optical character recognition (OCR) based speech synthesis system. The OCR based speech synthesis system has been developed using Laboratory virtual instruments engineering workbench (LabVIEW) 7.1.