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High Accuracy Optical Character Recognition Using Neural Networks with Centroid Dithering
February 1995 (vol. 17 no. 2)
pp. 218-224

Abstract—Optical character recognition (OCR) refers to a process whereby printed documents are transformed into ASCII files for the purpose of compact storage, editing, fast retrieval, and other file manipulations through the use of a computer. The recognition stage of an OCR process is made difficult by added noise, image distortion, and the various character typefaces, sizes, and fonts that a document may have. In this study a neural network approach is introduced to perform high accuracy recognition on multi-size and multi-font characters; a novel centroid-dithering training process with a low noise-sensitivity normalization procedure is used to achieve high accuracy results. The study consists of two parts. The first part focuses on single size and single font characters, and a two-layered neural network is trained to recognize the full set of 94 ASCII character images in 12-pt Courier font. The second part trades accuracy for additional font and size capability, and a larger two-layered neural network is trained to recognize the full set of 94 ASCII character images for all point sizes from 8 to 32 and for 12 commonly used fonts. The performance of these two networks is evaluated based on a database of more than one million character images from the testing data set.

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Index Terms:
Pattern recognition, optical character recognition, neural networks.
Hadar I. Avi-Itzhak, Thanh A. Diep, Harry Garland, "High Accuracy Optical Character Recognition Using Neural Networks with Centroid Dithering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 2, pp. 218-224, Feb. 1995, doi:10.1109/34.368165
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