A new segmentation algorithm for handwritten word recognition

Abstract

An algorithm for segmenting unconstrained printed and cursive words is proposed. The algorithm initially oversegments handwritten word images (for training and testing) using heuristics and feature detection. An artificial neural network (ANN) is then trained with global features extracted from segmentation points found in words designated for training. Segmentation points located in "test" word images are subsequently extracted and verified using the trained ANN. Two major sets of experiments were conducted, resulting in segmentation accuracies of 75.06% and 76.52%. The handwritten words used for experimentation were taken from the CEDAR CD-ROM. The results obtained for segmentation can easily be used for comparison with other researchers using the same benchmark database.