Publication

Abstract : In this paper, we present a novel scheme for text-independent online writer identification. As a first contribution, we propose histogram based features, inspired from the area of object detection, to describe the structural primitives of handwriting. Secondly, we have used sparse coding techniques to learn prototypes, that describe the general writing characteristics of the authors. To the best of our knowledge, the present proposal is the first of its kind that exploits the sparse learning framework for online writer identification. In addition, we consider the inclusion of ideas from information retrieval into our sparse representation to formulate a novel descriptor for each document. The efficacy of our proposal is tested on the handwritten paragraphs and text lines of the IAM On-Line Handwriting Database. We also provide a quantitative comparison of performance of our histogram based features with Fourier and Wavelet descriptors. The results are promising.