Comparative Analysis of Gabor and Discriminating Feature Extraction Techniques for Script Identification

A considerable amount of success has been achieved in developing monolingual OCR systems for Indian Scripts. But in a country like India, where many languages and scripts exist, it is more common that a single document contain words from more than one script. Therefore a script identification system is required to select the appropriate OCR. This paper presents a comparative analysis of two different feature extraction techniques for script identification of each word. In this work, for script identification discriminating and Gabor filter based features are computed of Punjabi words and English numerals. Extracted feature are simulated with Knn and SVM classifiers to identify the script and then recognition rates are compared. It has been observed that by selecting the appropriate value of k and appropriate kernel function with appropriate combination of feature extraction and classification scheme, there is significant drop in error rate.