A neural network approach to handprint character recognition

Jackel, L. D.; Stenard, C. E.; Baird, H. S.; Boser, B.; Bromley, J.; Burges, C. J. C.; Denker, J. S.; Graf, H. P.; Henderson, D.; Howard, R. E.; Hubbard, W.; leCun, Y.; Matan, O.; Pednault, E.; Satterfield, W.; Säckinger, E. and Thompson, T. (1991). A neural network approach to handprint character recognition. In: Compcon Spring'91: Digest of Papers, IEEE, pp. 472–475.

DOI: https://doi.org/10.1109/CMPCON.1991.128851


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.

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