Integrated Segmentation and Recognition of Handwritten Numerals: Comparison of Classification Algorithms

Authors:
Cheng-Lin Liu;Hiroshi Sako;Hiromichi Fujisawa
Affiliations:
-;-;-
Venue:
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Year:
2002

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A genetic framework using contextual knowledge for segmentation and recognition of handwritten numeral strings

Pattern Recognition

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Abstract

In integrated segmentation and recognition (ISR) of handwritten character strings, the underlying classifier is desired to be accurate in character classification and resistant to non-character patterns (also called garbage or outliers). This paper compares the performance of a number of statistical and neural classifiers in ISR. Each classifier has some variations depending on learning method: maximum likelihood estimation (MLE), discriminative learning (DL) under the minimum square error (MSE) or minimum classification error (MCE) criterion, or enhanced DL (EDL) with outlier samples. A heuristic presegmentation method is proposed to generate candidate cuts and character patterns.The methods were tested on the 5-digit Zip code images in CEDAR CDROM-1. The results show that training with outliers is crucial for neural classifiers in ISR. The best result was given by the learning quadratic discriminant function (LQDF) classifier.