Word Spotting in Gray Scale Handwritten Pashto Documents

Abstract

In this paper, we present an approach for word spotting in Gray-scale Pashto Documents, written in modified Arabic scripts. Various profile and transitional features are extracted from gray-scale word images. The gray-scale feature vectors are then converted into binary feature vectors by replacing each value within the gray-scale feature vectors with its binary equivalents. In this way, we have enabled the alignment of the gray-scale feature vectors via a faster binary pattern matching algorithm, i.e., Correlation Similarity Measure (CORR). The approach has effectively handled the handwriting variations of 200 different writers. The average precision rate achieved is 94.75 % for an average recall of 60.25%. The time taken for matching every set of two word images is 1.43 ms.