Cursive digit and character recognition in CEDAR database
Sameer Kumar Singh, Mark Hewitt · 2002
This paper uses a modified Hough transform method to extract features from the cursive handwritten digit and characters in CEDAR database. The technique does not require the detection of complex structural primitives such as loops. The handwriting images are divided into uniform regions that are analysed for the presence of horizontal, vertical and diagonal segments. The total number of such segments found in these regions are used as input to a linear classifier (discriminant analysis) and a nonlinear classifier (nearest neighbour). The results are produced on the complete test sets specified in CEDAR database as well as a leave-one-out cross-validation is performed. On the digit data, the results show a recognition rate of around 94% correct recognition on the test set and 87.5% using the leave-one-out method. Character recognition results range between 67% correct on a test set and 64% connect using leave-one-out cross-validation.