Handwritten Jawi words recognition using Hidden Markov Models

Abstract

Handwritten Jawi recognition is a challenging task because of the cursive nature of the writing. In manuscript writings, words are writer-dependent. The recognition task of Jawi Manuscript still opens problem due to the existence of many difficulties, such as the variability of character shape, overlap and presence of ligature in manuscript words. This paper describes a technique of Jawi word recognition using Hidden Markov Model (HMM). The technique of segmentation-free method used to transform word image into sequences of frames. The geometrical features are extracted using sliding window from each observation frame sequence. Besides, baseline parameters of Jawi word are use in the calculation of black pixel density. Vector Quantization clusters these features and assigns them into symbols that will be used as HMM input. Experiments have been conducted on 579 images of 100 words lexicon of Syair Rakis manuscript, and the recognition rate has reached 84 percent recognition.