• Journal of Information Processing Systems
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• v.9 no.1
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• pp.117-140
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• 2013

Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the document's textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.9-18
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• 2005

This paper proposes the color decomposition method for multi-primary display (MPD) using a 3-dimensional look-up-table (3D-LUT) in a linearized LAB space. The proposed method decomposes conventional three-primary colors into the multi-primary control values of a display device under constraints of tristimulus match. To reproduce images on the MPD, the color signals should be estimated from a device-independent color space, such as CIEXYZ and CIELAB. In this paper, the linearized LAB space is used due to its linearity and additivity in color conversion. The proposed method constructs the 3-D LUT, which contain gamut boundary information to calculate color signals of the MPD. For the image reproduction, standard RGB or CIEXYZ is transformed to the linearized LAB and then hue and chroma are computed to refer to the 3D-LUT. In the linearlized LAB space, the color signals of a gamut boundary point with the same lightness and hue of an input point are calculated. Also, color signals of a point on gray axis are calculated with the same lightness of an input. With gamut boundary points and input point, color signals of the input points are obtained with the chroma ratio divided by the chroma of the gamut boundary point. Specially, for the hue change, neighboring boundary points are employed. As a result the proposed method guarantees the continuity of color signals and computational efficiency, and requires less amount of memory.