Ankit Sharma

Hand geometry based recognition system

Hand geometry is one of the biometrics used to find practical use across the real-world security related applications. A hand geometry based recognition system works by capturing the image of a hand to determine its geometry and metrics namely the finger length, width and other attributes. Hand image segmentation is an important step in any hand geometry based recognition system. Because, the accuracy of the identified feature will be detected using a segmented hand image and it is totally depend upon the quality and accuracy of the segmented hand image.

Comparative analysis of zoning based methods for Gujarati handwritten numeral recognition

Gujarati is one of the ancient Indian languages spoken widely by the people of Gujarat state. This paper is concerned with the recognition of handwritten Gujarati numerals. For recognition of Gujarati numerals zoning based Feature extraction method is used. Numeral image is divided in 16×16, 8×8, 4×4 and 2×2 Zones. After feature extraction through the zoning method, Naive Bayes classifier and multilayer feed forward neural network classifier are implemented for the classification of numerals. For the database generation, 14,000 samples of each numeral are used. The overall recognition rates of this method used for recognition of Gujarati numeral using 16×16, 8×8, 4×4 and 2×2 zoning with neural network are 93.03%, 95.92%, 91.89% and 61.78% and with Naive Bayes classifier are 75%, 85.60%, 81% and 53.75% respectively.

A Novel Cross Correlation-Based Approach for Handwritten Gujarati Character Recognition

One of the major reasons for poor recognition rate in handwritten character recognition is the lack of unique features to represent handwritten characters. In this paper, an attempt is made to utilize the similarity already exist in different parts of the Gujarati characters. A novel feature extraction technique based on normalized cross correlation is proposed for handwritten Gujarati character recognition. An overall accuracy of 53.12%, 68.53%, and 66.43% is obtained using Naive Bayes classifier, linear and polynomial Support Vector Machine (SVM) classifiers, respectively, with the proposed feature extraction algorithm. Experimental results show significant contribution by proposed technique and improvement in recognition rate may be obtained by combining these features with some other significant features. One of the significant contributions of proposed work is the development of large and representative dataset of 20,500 isolated handwritten Gujarati characters.

Gujarati handwritten numeral recognition through fusion of features and machine learning techniques

Languages have played a major role in Indian history and they continue to influence the lives of Indians till date. Plentiful research on optical character recognition (OCR) techniques for Indian languages such as Hindi, Tamil, Bangla, Kannada, Gurumukhi, Malayalam and Marathi has already been carried out. Research efforts on Gujarati character recognition are few and yet to gain momentum. This paper intends to bring Gujarati character recognition in attention. Methods based on artificial neural network (ANN), support vector machine (SVM) and naive Bayes (NB) classifier are exercised for handwritten Gujarati numerals recognition. Experiments are carried out on two large datasets using three different kinds of features and their fusion. Zone-based, projection profiles-based and chain code-based features are employed as individual features. The paper proposes to use a fusion of these features for learning prediction models. Experimental results show significant improvement over state-of-the-art and validate our proposals.

Chain code feature based recognition of handwritten Gujarati numerals

This paper describes chain code based method for handwritten Gujarati numeral recognition. Literature review on Indian OCR indicates that in comparison with Bangla, Hindi, Kannada, Tamil and Telugu scripts, the OCR activities related to Gujarati script is very less. Development of OCR for Gujarati script is quite challenging area for research. In this work, recognition of isolated Gujarati handwritten numerals is performed using chain code based methods. Horizontal scanning and maximum distance from centroid methods are used for deciding the starting point for calculating the chain code sequence. An overall accuracy of 96.37% and 95.62% is obtained using feed forward neural network classifier by the proposed methods respectively. One of the significant contributions of this paper is towards the generation of large and representative database for handwritten Gujarati numerals. Keywords:Chain code, Gujarati handwritten numeral recognition, Neural network classifier.