5,498 research outputs found
Feedback Based Architecture for Reading Check Courtesy Amounts
In recent years, a number of large-scale applications continue to rely heavily on the use of paper as the
dominant medium, either on intra-organization basis or on inter-organization basis, including paper
intensive applications in the check processing application. In many countries, the value of each check is
read by human eyes before the check is physically transported, in stages, from the point it was presented
to the location of the branch of the bank which issued the blank check to the concerned account holder.
Such process of manual reading of each check involves significant time and cost. In this research, a new
approach is introduced to read the numerical amount field on the check; also known as the courtesy
amount field. In the case of check processing, the segmentation of unconstrained strings into individual
digits is a challenging task because one needs to accommodate special cases involving: connected or
overlapping digits, broken digits, and digits physically connected to a piece of stroke that belongs to a
neighboring digit. The system described in this paper involves three stages: segmentation, normalization,
and the recognition of each character using a neural network classifier, with results better than many other
methods in the literaratu
Handwritten Bank Check Recognition of Courtesy Amounts
In spite of rapid evolution of electronic techniques, a number of large-scale applications continue to rely on the use
of paper as the dominant medium. This is especially true for processing of bank checks. This paper examines the
issue of reading the numerical amount field. In the case of checks, the segmentation of unconstrained strings into
individual digits is a challenging task because of connected and overlapping digits, broken digits, and digits that are
physically connected to pieces of strokes from neighboring digits. The proposed architecture involves four stages:
segmentation of the string into individual digits, normalization, recognition of each character using a neural network
classifier, and syntactic verification. Overall, this paper highlights the importance of employing a hybrid architecture
that incorporates multiple approaches to provide high recognition rates
Hexagonal structure for intelligent vision
Using hexagonal grids to represent digital images have been studied for more than 40 years. Increased processing capabilities of graphic devices and recent improvements in CCD technology have made hexagonal sampling attractive for practical applications and brought new interests on this topic. The hexagonal structure is considered to be preferable to the rectangular structure due to its higher sampling efficiency, consistent connectivity and higher angular resolution and is even proved to be superior to square structure in many applications. Since there is no mature hardware for hexagonal-based image capture and display, square to hexagonal image conversion has to be done before hexagonal-based image processing. Although hexagonal image representation and storage has not yet come to a standard, experiments based on existing hexagonal coordinate systems have never ceased. In this paper, we firstly introduced general reasons that hexagonally sampled images are chosen for research. Then, typical hexagonal coordinates and addressing schemes, as well as hexagonal based image processing and applications, are fully reviewed. © 2005 IEEE
Character Recognition
Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field
Anonimização automatizada de contratos jurídicos em português
With the introduction of the General Data Protection Regulation, many organizations
were left with a large amount of documents containing public information
that should have been private. Given that we are talking about quite large quantities
of documents, it would be a waste of resources to edit them manually. The
objective of this dissertation is the development of an autonomous system for the
anonymization of sensitive information in contracts written in Portuguese.
This system uses Google Cloud Vision, an API to apply the OCR tecnology, to
extract any text present in a document. As there is a possibility that these documents
are poorly readable, an image pre-processing is done using the OpenCV
library to increase the readability of the text present in the images. Among others,
the application of binarization, skew correction and noise removal algorithms were
explored.
Once the text has been extracted, it will be interpreted by an NLP library. In this
project we chose to use spaCy, which contains a Portuguese pipeline trained with
the WikiNer and UD Portuguese Bosque datasets. This library not only allows a
very complete identification of the part of speech, but also contains four different
categories of named entity recognition in its model. In addition to the processing
carried out using the spaCy library, and since the Portuguese language does not
have a great support, some rule-based algorithms were implemented in order to
identify other types of more specific information such as identification number and
postal codes. In the end, the information considered confidential is covered by
a black rectangle drawn by OpenCV through the coordinates returned by Google
Cloud Vision OCR and a new PDF is generated.Com a introdução do Regulamento Geral de Proteção de Dados, muitas organizações
ficaram com uma grande quantidade de documentos contendo informações
públicas que deveriam ser privadas. Dado que estamos a falar de quantidades
bastante elevadas de documentos, seria um desperdício de recursos editá-los manualmente.
O objetivo desta dissertação é o desenvovimento de um sistema autónomo
de anonimização de informação sensível em contratos escritos na língua
Portuguesa.
Este sistema utiliza a Google Cloud Vision, uma API de OCR, para extrair qualquer
texto presente num documento. Como existe a possibilidade desses documentos
serem pouco legíveis, é feito um pré-processamento de imagem através da biblioteca
OpenCV para aumentar a legibilidade do texto presente nas imagens. Entre
outros, foi explorada a aplicação de algoritmos de binarização, correção da inclinação
e remoção de ruído.
Uma vez extraído o texto, este será interpretado por uma biblioteca de nlp, neste
projeto optou-se pelo uso do spaCy, que contém um pipeline português treinado
com os conjuntos de dados WikiNer e UD Portuguese Bosque. Esta biblioteca
não permite apenas uma identificação bastante completa da parte do discurso,
mas também contém quatro categorias diferentes de reconhecimento de entidade
nomeada no seu modelo. Para além do processamento efetuado com o recurso à
biblioteca de spaCy, e uma vez que a língua portuguesa não tem um grande suporte,
foram implementados alguns algoritmos baseados em regras de modo a identificar
outros tipos de informação mais especifica como número de identificação e códigos
postais. No final, as informações consideradas confidenciais são cobertas por um
retângulo preto desenhado pelo OpenCV através das coordenadas retornadas pelo
OCR do Google Cloud Vision e será gerado um novo PDF.Mestrado em Engenharia de Computadores e Telemátic
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