A two-stage classifier for broken and blurred digits in forms

A classifier for an automatic system that recognizes multifont typewritten digits, often broken and blurred, in forms is presented. The classification, which is based on the utilization of a global feature, is applied in two phases. Firstly, a minimum distance method (1-NN) is applied in a multifont classifier to provide a global classification of the patterns in a form. A problem associated to multifont classifiers is the interference among classes in different fonts. An interesting aspect of this particular application is that it is highly probable that a form includes just one font. Then, in the second phase, a specialized classifier, oriented to one-form, uses the patterns in the form previously classified to validate, or reject and reclassify them, on the basis of the mean distance to the predefined classes. This specialized classifier affords significant improvement in performance. A classification accuracy rate of 99.42% has been achieved.

[1]  Patrick K. Simpson,et al.  Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations , 1990 .

[2]  Fang-Hsuan Cheng,et al.  Recognition of handprinted chinese characters via stroke relaxation , 1993, Pattern Recognit..

[3]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[4]  Sargur N. Srihari,et al.  High-performance reading machines , 1992 .

[5]  Ching Y. Suen,et al.  Historical review of OCR research and development , 1992, Proc. IEEE.

[6]  Zheru Chi,et al.  Handwritten numeral recognition using self-organizing maps and fuzzy rules , 1995, Pattern Recognit..

[7]  Maher A. Sid-Ahmed,et al.  Fast learning and efficient memory utilization with a prototype based neural classifier , 1995, Pattern Recognit..

[8]  Patrick J. Grother,et al.  The Second Census Optical Character Recognition Systems Conference , 1994 .

[9]  Osamu Hori,et al.  A robust recognition system for a drawing superimposed on a map , 1992, Computer.

[10]  Gérard Dreyfus,et al.  Handwritten digit recognition by neural networks with single-layer training , 1992, IEEE Trans. Neural Networks.

[11]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[12]  Patrick J. Grother,et al.  Massively parallel implementation of character recognition systems , 1992, Electronic Imaging.

[13]  Belur V. Dasarathy,et al.  Nearest neighbor (NN) norms: NN pattern classification techniques , 1991 .

[14]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[15]  S. Impedovo,et al.  Optical Character Recognition - a Survey , 1991, Int. J. Pattern Recognit. Artif. Intell..

[16]  Lawrence D. Jackel,et al.  Reading handwritten digits: a ZIP code recognition system , 1992, Computer.

[17]  M. Berthod,et al.  Automatic recognition of handprinted characters—The state of the art , 1980, Proceedings of the IEEE.