Recognition of archaic Lanna handwritten manuscripts using a hybrid bio-inspired algorithm

Abstract

This paper proposes a new character recognition system for archaic Lanna handwritten characters. The proposed system consists of two main processes: the segmentation process and the recognition process. The segmentation process decomposes the touching or overlapping characters, commonly found in Lanna manuscripts, into isolated characters. In the recognition process, the proposed system uses the self-organizing map to divide the input images into several clusters. The clonal selection algorithm of the artificial immune system is then used to build a recognition model for each cluster created by the self-organizing map. Finally, the particle swarm optimization is employed as a local search mechanism. The proposed system was evaluated and compared to several state-of-the-art approaches. The experimental results demonstrate that the proposed system is very effective in recognizing not only Lanna characters but also the handwritten numerals of the five most popular scripts.