A Low Level Feature Based Neural Network Segmenter for Fully Cursive Handwritten Words

We describe a neural network for segmentation of handwritten words. Typical approaches to segmentation rely on “over segmentation” of the word using simple features. Vocabulary context can then be used to recover the correct segmentation points. However in large vocabulary applications it is much more important to have high quality segmentation points in order to reduce the number of alternative candidate words to be considered. The system is constructed using supervised training, and gives improved accuracy for segmentation. Results compare segmentation accuracy with the number of possible segmentation points that are considered, and show that only a small number of “excess” segmentation points are necessary.