Engineering Commons^™

Partitioning Of The Degradation Space For Ocr Training, Elisa H. Barney Smith, Tim Andersen

Electrical and Computer Engineering Faculty Publications and Presentations

Generally speaking optical character recognition algorithms tend to perform better when presented with homogeneous data. This paper studies a method that is designed to increase the homogeneity of training data, based on an understanding of the types of degradations that occur during the printing and scanning process, and how these degradations affect the homogeneity of the data. While it has been shown that dividing the degradation space by edge spread improves recognition accuracy over dividing the degradation space by threshold or point spread function width alone, the challenge is in deciding how many partitions and at what value of edge …