A neural network approach to handprint character recognition

The authors outline OCR (optical character recognition) technology developed at AT&T Bell Laboratories, including a recognition network that learns feature extraction kernels and a custom VLSI chip that is designed for neural-net image processing. It is concluded that both high speed and high accuracy can be obtained using neural-net methods for character recognition. Networks can be designed that learn their own feature extraction kernels. Special-purpose neural-net chips combined with digital signal processors can quickly evaluate character-recognition neural nets. This high speed is particularly useful for recognition-based segmentation of character strings.<<ETX>>

[1]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[2]  D. Rumelhart.  Learning internal representations by back-propagating errors , 1986 .

[3]  Theodosios Pavlidis,et al.  On the Recognition of Printed Characters of Any Font and Size , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Lawrence D. Jackel,et al.  Large Automatic Learning, Rule Extraction, and Generalization , 1987, Complex Syst..

[5]  H.P. Graf,et al.  A reconfigurable CMOS neural network , 1990, 1990 37th IEEE International Conference on Solid-State Circuits.

[6]  Yann LeCun,et al.  Handwritten zip code recognition with multilayer networks , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[7]  E. Sackinger,et al.  An Analog Neural Network Processor With Programmable Network Topology , 1991, 1991 IEEE International Solid-State Circuits Conference. Digest of Technical Papers.