A novel multiple experts and fusion based segmentation algorithm for cursive handwriting recognition

This paper presents a novel segmentation algorithm for offline cursive handwriting recognition. An over-segmentation algorithm is introduced to dissect the words from handwritten text based on the pixel density between upper and lower baselines. Each segment from the over-segmentation is passed to a multiple expert-based validation process. First expert compares the total foreground pixel of the segmentation point to a threshold value. The threshold is set and calculated before the segmentation by scanning the stroke components in the word. Second expert checks for closed areas such as holes. Third expert validates segmentation points using a neural voting approach which is trained on segmented characters before validation process starts. Final expert is based on oversized segment analysis to detect possible missed segmentation points. The proposed algorithm has been implemented and the experiments on cursive handwritten text have been conducted. The results of the experiments are very promising and the overall performance of the algorithm is more effective than the other existing segmentation algorithms.