Recognizing Overlapping Hand-Printed Characters by Centered-Object Integrated Segmentation and Recognition

Abstract

This paper describes an approach, called centered object integrated seg(cid:173) mentation and recognition (COISR). for integrating object segmenta(cid:173) tion and recognition within a single neural network. The application is hand-printed character recognition. 1\vo versions of the system are described. One uses a backpropagation network that scans exhaus(cid:173) tively over a field of characters and is trained to recognize whether it is centered over a single character or between characters. When it is centered over a character, the net classifies the cnaracter. The approach is tested on a dataset of hand-printed digits. Vel)' low errOr rates are reported. The second version, COISR-SACCADE, avoids the need for exhaustive scans. The net is trained as before. but also is trained to compute ballistic 'eye' movements that enable the input window to jump from one character to the next.