Handwritten character recognition is used frequently to describe the ability of computer to translate human writing into text. The technical challenges in character recognition arise mainly from three sources. First the symbols: the set of idealized shapes that can occur, often in a hierarchy where simple symbols are assembled into more complex ones, at several levels of organization. Second is deformation: the range of shape variations that each symbol is allowed to undergo, including geometric transformation (translation, rotation, scaling, Stretching, etc.) and more complex or time dependent distortion. Third is an image defect: the imperfections in image due to printing, optics, scanning, quantization, binarization [1], etc. Handwriting and machine print demand a different approach. Handwriting consists of elongated strokes, whereas the machine print consists of regularly spaced blobs. Approx. 500 million people around the world use Devnagari script. It provides written form to over forty languages which
[1] N.ARICA, F.T.Y. VURAL, “AN OVERVIEW OF CHARACTER RECOGNITION FOCUSED ON OFFLINE HANDWRITING”, IEEE TRANS ON SYSTEM ,MAN,CYBERNATICS-PARTC, VOL 31,N0.2(2001)
[2] OVIND TRIER, ANIL JAIN AND TORFINN TAXT,” A FEATURE EXTRACTION METHODS FOR CHARACTER RECOGNITION-A SURVEY”, PATTERN RECOGNITION, VOL 29, NO-4, AND PP 641-662, 1996
.[3] U.PAL AND B.B. CHAUDHURI,” AN IMPROVED DOCUMENT SKEW ANGLE ESTIMATION TECHNIQUES”, PATTERN RECOGNITION LETTERS 17:899-904, 1996. [3] B.B. CHAUDHRURI AND U.PAL, “A COMPLETE PRINTED OCR”, PATTERN RECOGNITION,(5):531-549, 1998.
[4] REJEAN PLAMONDON AND SARGUR N. SRIHARI, “ON-LINE AND OFF-LINE HANDWRITTEN RECOGNITION” A COMPREHENSIVE SURVEY”, IEEE PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL 22, NO. 1, JANUARY 2000.
[5] U. PAL, T. WAKABAYASHI, F. KIMURA, “COMPARATIVE STUDY OF DEVNAGARI HANDWRITTEN CHARACTER RECOGNITION USING DIFFERENT FEATURE AND CLASSIFIERS”, 10TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION 2009.
[6] ZHIYI ZHANG, LIANWEN JIN, KAI DING, XUE GAO,”CHARACTERSIFT: A NOVEL FEATURE FOR OFFLINE HANDWRITTEN CHINESE CHARACTER RECOGNITION” 10TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION, 2009
[7] T.V.ASWIN AND P S SASTRY, “A FONT AND SIZE-INDEPENDENT OCR SYSTEM FOR PRINTED KANNADA DOCUMENTS USING SUPPORT VECTOR MACHINES”, SADHANA VOL.27.PART I, PP.35-58, FEBRUARY 2002.
[8] T.V.ASWIN, “A FONT INDEPENDENT OCR FOR PRINTED KANNADA USING SVM”, MASTER THESIS, INDIAN INSTITUTE OF SCIENCE, BANGALORE, 2000.
[9] VEENA BANSAL AND R. M. K. SINHA, “A DEVANAGARI OCR AND A BRIEF OVERVIEW OF OCR RESEARCH FOR INDIAN SCRIPTS”, PROCEEDINGS OF STRANS01, IIT KANPUR 2001
