IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 8, Issue 6 (Jan. - Feb. 2013), PP 43-48 www.iosrjournals.org www.iosrjournals.org 43 | Page Hand Written Bangla Numerals Recognition for Automated Postal System Mostofa Kamal Nasir 1 and Mohammad Shorif Uddin 2 1 Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Bangladesh 2 Department of Computer Sceince and Engineering, Jahangirnagar University, Bangladesh Abstract: Recognition of handwritten Bangla numerals finds numerous applications in postal system automation, passports and document analysis and even for number plate identification. However, the recognition rate requires high and reliable accuracy for practical applications. This paper delineate a robust hybrid system for recognition of handwritten Bangla numerals for the automated postal system, which performed feature extraction using k-means clustering, Baye’s theorem and Maximum a Posteriori, then the recognition is performed using Support Vector Machine . Recognition of handwritten numerals, such as postal codes, reveal all kinds of local and global deformations: distortions, different writing styles, thickness variations, wide variety of scales, limited amount of rotation, added noise, occlusion and missing parts . This paper shows that the proposed method is better than other system. Keywords - K-means clustering, Bayes’ theorem, MAP, PCA, SVM and OCR. I. Introduction Optical character recognition (OCR) is an automatic reading of optically sensed document text materials to translate human-readable characters to machine-readable codes. OCR system for printed Bangla numerals, the fourth most popular script in the world [1]. About 200 million people of Eastern India and Bangladesh use Bangla as language. Postal automation is a topic of research interest for last two decades. System development towards postal automation for a country like Bangladesh is more difficult than that of other countries because of its multi-script behavior. Some people write the destination address part of a postal document in two language scripts (i.e. Mixing Bangla and English). Unfortunately, researches on Bangla numeral recognition are not sufficient so far, in particular on handwritten issue. Some Papers on printed Bangla numeral recognition have been reported in past years [1-3]. But there are few researches on handwritten Bangla numeral recognition. Professor Pal. have done some exploring work for the issue of recognizing handwritten Bangla numerals [2-4]. Another well known research work is presented in [5]. In this paper, we propose a knowledge based system for handwritten Bangla numerals detection and shows its implementation of postal automation. This paper also shows the comparison between existing methods and the proposed method. The rest of the paper is organized as follows: Existing Method is discussed in Section 2. In Section 3, the flowchart of the hybrid system is showed. The proposed method is described in Section 4. Section 5 presents Result and Comparison with Existing System which is followed by conclusions and future works in Section 6. II. Existing Method The handwritten digit recognition application is a machine vision task. The input consists of black or white pixels. The digits are usually well separated from the background and there are only ten output categories. Yet the problem deals with objects in a real two dimensional space and the mapping from image space to category space has both considerable regularity and considerable complexity. The problem has added attraction because it is of great practical value. The database used to train and test the network contains handwriting of many different people. The choice of an appropriate data representation is a crucial point when solving a classification task, either with a trainable or with a nontrainable classifier. Therefore, the original input representation is usually transformed into a higher-level data representation by using human expertise for designing appropriate preprocessing operations. However, besides mere recognition rates, there are other factors which may influence the choice of the data representation, such as the need for high computation speed or hardware limitations, favoring data representations which do not require complicated and time consuming preprocessing. In many cases, this precludes the use of structured data representations. Therefore, the first data representation we use for the subsequent classification is a simple pixel representation. For a given data representation, an optimal hyper surface separating the classes in the N dimensional input space in the best possible way might be found if the underlying probability distributions were either known or estimated accurately. In the case of handwritten digits, it seems therefore natural to use some a priori knowledge about the recognition task in order to transform the low level information of the pixel images into a data representation of