|| Bioinfo Publications || 421 International Journal of Machine Intelligence ISSN: 0975-2927 & E-ISSN: 0975-9166, Volume 5, Issue 1, 2013, pp.-421-425. Available online at http://www.bioinfopublication.org/jouarchive.php?opt=&jouid=BPJ0000231 CHAVAN S.V. 1 *, KALE K.V. 2 , KAZI M.M. 2 AND RODE Y.S. 2 1 Department of CSIT, MSS’s ACS College, Ambad, Jalna- 431 213, MS, India. 2 Department of CSIT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad- 431 004, MS, India. *Corresponding Author: Email- shripc@gmail.com Received: March 30, 2013; Accepted: April 15, 2013 Introduction Handwritten character recognition is the important area in image processing and pattern recognition fields. This field of research is applicable to various application areas where the aim is too atom- ized and reduces the human efforts for form filling, job application, bank and postal automation [1-3] etc. Handwritten character recog- nition in Indian script [4] is a challenging task specially Devanagari, for several reason like complex structure of character with their modifiers and present of compound character. Compound character are those where two or more character are joined together to pro- duce a special character. These characters are such type in which one half of character is connected to full character. Thus there are large variations in shape of character as writing style, pen quality (thick/thin), strokes that substantial extent the recognition accuracy. Writing style in Devanagari script is from left to right. The concept of upper/lower case is absent in Devanagari script. In Devanagari script a vowel following a consonant takes a modified shape. De- pending on the vowel, its modified shape is placed at the left, right (or both) or bottom of the consonant. These modified shapes are called modified characters. A consonant or vowel following a conso- nant sometimes takes a compound orthographic shape, which we call as compound character. Work on Devanagari was started early in 1970. Sinha and Mahaba- la [5] presented a syntactic pattern analysis system for the recogni- tion of handwritten and machine printed Devnagari characters. OCR work for printed and handwritten characters in various Indian scripts [6-8] is carried out by researchers but major work is found for Bang- la [9,10] and Devanagari. First research report on handwritten De- vanagari character was published in 1977 by Sethi and Chatterjee [11]. They proposed an MLP neural network-based classification approach for the recognition and obtained 91.28% results. An ex- tensive research work on printed Devanagari was carried out by Bansal and Sinha[12-14]. First, Handwritten numerals of Devanaga- ri work was presented by Bajaj, Dey and Chaudhury [15] proposed a multi-classifier connectionist architecture for recognition and they obtained 89.6% accuracy. Patil and Sontakke [16] presented an algorithm for handwritten Devanagari numerals recognition which was rotation, scale and translation invariant using Fuzzy Neural Network. Hanmandlu and Murty [17] who proposed Fuzzy model- based recognition of Handwritten Hindi Numerals and they obtained 92.67% accuracy. Kumar and Singh [18] proposed a Zernike mo- ment-based technique and obtained 80% recognition rate. Sharma and Pal [19] made an important contribution using Chain Code His- togram with an accuracy of 80.36%. Hanmandlu and Murty pub- lished a Fuzzy-based system [20] and achieved 90.65% accuracy. Pal, Sharma, T. Wakabayashi and Kimura [21] proposed a method based on directional information obtained from the arc tangent of the gradient yielding 94.24% recognition rate. Another significant contribution is due to Arora and Bhattacharjee in which they pro- posed a multi-feature extraction based technique thereby achieving 92.8% accuracy [22]. Pal and T. Wakabayashi proposed SVM and Citation: Chavan S.V., et al. (2013) Recognition of Handwritten Devanagari Compound Character a Moment Feature Based Approach. Interna- tional Journal of Machine Intelligence, ISSN: 0975-2927 & E-ISSN: 0975-9166, Volume 5, Issue 1, pp.-421-425. Copyright: Copyright©2013 Chavan S.V., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited. International Journal of Machine Intelligence ISSN: 0975-2927 & E-ISSN: 0975-9166, Volume 5, Issue 1, 2013 Abstract- The recognition of Handwritten Devanagari character plays a vital role in the research area. Number of approaches has been used in preceding researches and still it is being carried out ahead. In this research paper, Geometric and Zernike moment features of Devanagari basic and compound Character are used to recognize the handwritten character. Compound character is a special feature of Devanagari scripting; it joins two or more character in various ways forming a new character. The complexity and frequency occur in writing the compound character is more as compared to other languages. The proposed system is trained and tested on 27000 handwritten Devanagari basic and compound character database collected from different people. Each image is normalized to 30X30 pixel size. For recognition of Devanagari basic and compound character we have used MLP and KNN. The recognition rate is 98.78% and 95.56% which is comprehensive to the meth- od. Keywords- Handwritten Devanagari compound character, Geometric moment, Zernike Moment, MLP KNN. RECOGNITION OF HANDWRITTEN DEVANAGARI COMPOUND CHARACTER A MOMENT FEATURE BASED APPROACH