Offline Jawi handwritten recognizer using hybrid artificial neural networks and dynamic programming
Anton Heryanto, Mohammad Faidzul Nasrudin, Khairuddin Omar · 2008
This paper describes an offline Jawi handwritten recognizer using hybrid Artificial Neural Networks (ANN) as the character recognizer and Viterbi Dynamic Programming as verifier. We use a recognition-based segmentation approach to solve character segmentation problems. Segmented sub words images are segmented into a fixed width slices. The combinations of the slices form a segmentation graph. Two-layers of Back Propagation Neural Networks compute probabilities for each character hypotheses in the segmentation graph. Viterbi Dynamic Programming selects the maximum average probability of a character hypothesis combination from all possibility in segmentation graph. This system evaluates against selected word from a Jawi handwritten manuscripts. Recognition performance of the character in words presented.