Optical character recognition (OCR) is a very popular research area since 1950's. Many people has done a lot of work on various scripts. Line segmentation is a very important step in OCR as the accuracy of the recognition algorithm highly depends on the correct line segmentation. Incorrect line segmentation not only decreases the accuracy but also may lead to some other errors. The objective of...

This paper presents a features extraction module for isolated handwritten Arabic characters. The collected core features are based on pixels orientations according to Freeman chain code. The input to this module is Arabic character (in its basic-shapes i.e. without diacritics). The features extractor module, fed with a skeleton of an isolated character basic-shape, yields global and local featu...

A document page may contain two or more different scripts. For Optical Character Recognition (OCR) of such a document page, it is necessary to separate different scripts before feeding them to their individual OCR system. In this paper an automatic scheme is presented to identify text lines of different Indian scripts from a document. For the separation task at first the scripts are grouped int...

This PhD thesis investigates the image sequence labeling problems optical character recognition (OCR), object tracking, and automatic sign language recognition (ASLR). To address these problems we investigate which concepts and ideas can be adopted from speech recognition to these problems. For each of these tasks we propose an approach that is centered around the approaches known from speech r...

Among the vast range of off–line optical character recognition applications is the machine processing of forms. The objective is to imitate the human ability to read but at higher speed. This paper presents a neural network based system to recognise handwritten Arabic characters collected from more than 500 forms. Wavelet coefficients extracted from the character samples are used to tune the ne...

We propose a novel learning method combining query learning and a “genetic translator” we developed. Query learning is a useful technique for high-accuracy, high-speed learning. However, it has not been applied for practical optical character readers (OCRs), since human beings cannot recognize queries in the feature space used in practical OCR devices. We previously proposed a character image r...

An image-based document translation system consists of several components, among which OCR (Optical Character Recognition) plays an important role. However, existing OCR software is not robust against environmental variations. Furthermore, OCR errors are often propagated into the translation component and cause, causing poor end-to-end performance. In this paper, we propose an imagebased docume...

Machine Translation (MT) plays a critical role in expanding capacity in the translation industry. However, many valuable documents, including digital documents, are encoded in non-accessible formats for machine processing (e.g., Historical or Legal documents). Such documents must be passed through a process of Optical Character Recognition (OCR) to render the text suitable for MT. No matter how...

As demand grows for mobile applications, research in optical character recognition (OCR), a technology well-developed for document imaging, is shifting focus to the recognition of text embedded in digital photographs or video. Segmenting text and background in natural scenes is a difficult classification problem, and the accuracy of this segmentation is of utmost importance when the output of a...

Handwritten numeral recognition is in general a benchmark problem of Pattern Recognition and Artificial Intelligence. Compared to the problem of printed numeral recognition, the problem of handwritten numeral recognition is compounded due to variations in shapes and sizes of handwritten characters. Considering all these, the problem of handwritten numeral recognition is addressed under the pres...