Over-segmentation and validation strategy for off-line cursive handwriting recognition

Abstract

This paper presents an over-segmentation and validation strategy for off-line cursive handwriting recognition. Over-segmentation module is employed to find all the possible character boundaries. Then, the incorrect segmentation points from over-segmenting module are removed by validating processes. The over-segmentation was performed based on the vertical pixel density between upper and lower baselines. Wherever the pixel density is less than threshold, an over-segmentation point is assigned. After the over-segmentation is done, validation starts removing over-segmentation points. The first validation module checks if a segmentation point lies in hole region. The second validation module compares total foreground pixel between two neighbouring segmentation points to a threshold value. The third validation module is neural network voting by neural network classifier trained on pre-segmented characters. Finally, the oversized segment validation process checks if there is any missing segmentation point between neighbouring characters. The proposed approach has been implemented, and the experiments on CEDAR benchmark database 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.