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In this paper, we are proposing a new semantic and contextual based document image classification framework. The framework is composed of two main modules. The first one is the text analysis module (TAM) which processes document images and extracts words from the image, and second one is the SEMCON, which is a semantic and contextual objective metric. From the list of extracted words by TAM, SEMCON...
Text is a vital feature in applications of computer vision. Traditional Chinese character recognition techniques are mainly based on optical character recognition (OCR), however, they can't obtain satisfactory results from images affected by complex circumstance, such as different viewpoint, scale changes, addition of noise and complex background. To solve these problems, inspired by SIFT descriptor,...
In a multi-script multi-lingual environment, a document may contain text lines in more than one script/language forms. It is necessary to identify different script regions of the document in order to feed the document to the OCRs of individual language. With this context, this paper proposes to develop a monothetic algorithmic model to identify and separate text lines Telugu, Hindi and English scripts...
It is known that convolutional neural networks (CNNs) are efficient for optical character recognition (OCR) and many other visual classification tasks. This paper applies error-correcting output coding (ECOC) to the CNN for segmentation-free OCR such that: 1) the CNN target outputs are designed according to code words of length N; 2) the minimum Hamming distance of the code words is designed to be...
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