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 processing and pattern recognition. It can be considered as one of the benchmarks in evaluating feature extraction methods and the performance of classifiers. The performance of character recognition system depends heavily on what kind of features are being used. The objective of this paper is to provide efficient and reliable techniques for recognition of handwritten numerals. In this paper we propose Zoning based feature extraction system which calculates the densities of object pixels in each zone. Firstly the whole image is divided into 4 4 zones. Further in order to gain more accuracy these zones are divided into 6 6 zones. The division of zones carried out up to 8 8 zones. Hence 116 features are extracted in all. Nearest neighbour classifier is used for subsequent classification and recognition purpose.

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 and English. The work presented in this thesis, focuses on the problem of recognition of isolated handwritten characters in Gurmukhi script. The whole process consists of two stages. The first, feature extraction stage analyzes the set of isolated characters and selects a set of features that can be used to uniquely identify characters. The performance of recognition system depends heavily on what features are being used. The selection of stable and representative set of features is the heart of recognition system. The feature extraction method Zoning, is used for extracting features of the character under consideration in this problem. In Zoning method, the frame containing the character is divided into several overlapping or non-overlapping zones and the densities of object pixels in each zone are calculated. Densities are used to form a representation. The final, classification stage is the main decision making stage of the recognition system. It uses features extracted in the feature extraction stage to identify the character. K-Nearest Neighbor and Support Vector Machine are the two classifiers used for identifying the character in the problem. In k-nearest classification method, the Euclidean distance between the test point and all the reference points is calculated in order to find K nearest neighbors, and then the obtained distances are ranked in ascending order and the reference points corresponding to the k smallest Euclidean distances are taken. The Support Vector Machine (SVM) is learning machine with very good generalization ability. SVM implements the Structural Risk Minimization Principal which seeks to minimize an upper bound of the generalization error. An SVM classifier discriminates two classes of feature vectors by generating hyper-surfaces in the feature space, which are "optimal" in a specific sense that is the hyper-surface obtained by the SVM optimization is guaranteed to have the maximum distance to the "nearest" support vectors. SVM operate on kernel evaluations of the feature vectors. An annotated sample image database of isolated handwritten characters in Gurmukhi script has been prepared which has been used for training and testing of the system.

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 learned and unlearned images of characters. Learned images have recognition accuracy as 91.77 % and unlearned images have recognition accuracy as 93.79 %. The overall recognition accuracy for both learned and unlearned Gurmukhi characters are 92.78 %. This confirms that the proposed neocognitron artificial neural network approach is suitable for the development of isolated handwritten characters of Gurumukhi script.

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 accuracy of 72.6% has been achieved with the use of the algorithms for segmenting all types of words. Segmentation accuracy of 88.1% has been achieved for segmenting all types of handwritten words in Gurmukhi script. Further, different categories of overlapping and touching characters in all the three zones (upper, middle and lower zone) of handwritten words in Gurmukhi script have been identified on the basis of structural properties of Gurmukhi script. A method for segmenting overlapping characters in middle zone has been proposed.

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