Integrated Segmentation and Recognition of Hand-Printed Numerals

Neural network algorithms have proven useful for recognition of individual, segmentedcharacters. However, their recognition accuracy has been limited by the accuracy of the underlying segmentation algorithm. Conventional, rule-basedsegmentation algorithms encounter difficulty if the characters are touching, broken, or noisy. The problem in these situations is that often one cannot properly segment a character until it is recognized yetone cannot properly recognize a character until it is segmented. We present here a neural network algorithm that simultaneously segments and recognizes in an integrated system. This algorithm has several novel features: it uses a supervised learning algorithm (backpropagation), but is able to take position-independent information as targets and self-organize the activities of the units in a competitive fashion to infer the positional information. We demonstrate this ability with overlapping hand-printed numerals.