Dharamveer Sharma

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Isolated handwritten character recognition has been the subject of intensive research during last decades because it is useful in wide range of real world problems. It also provides a solution for processing large volumes of data automatically. Work has been done in recognizing handwritten characters in many languages like Chinese, Arabic, Devnagari, Urdu(More)
This paper presents the development of Gurumukhi character recognition system of isolated handwritten characters by using Neocognitron at the first time. Well-known neocognitron artificial neural network is chosen for its fast processing time and its good performance for pattern recognition problems. Here we have found the recognition accuracy of both(More)
This paper presents a new approach to off-line handwritten numeral recognition. From the concept of perturbation due to writing habits and instruments, we propose a recognition method which is able to account for a variety of distortions due to eccentric handwriting. The recognition of handwritten numerals is a challenging task in the field of image(More)
Segmentation of handwritten words is a challenging task primarily because of structural features of the script and varied writing styles. Handwritten words are also prone to the problem of overlapped, connected, merged and broken characters. Based on certain properties of Gurmukhi script, different zones across the height of word are detected. Segmentation(More)
Nowadays, the accumulation of paper in the life of business professional is overwhelming. Digital documents on the other hand being less expensive and more efficient are on the road to a more organized office. Also crucial for many business applications document image analysis is needed before OCR operation. Segmentation of document images into text and(More)
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