A structural/statistical feature based vector for handwritten character recognition
Pattern Recognition Letters
A Novel Feature Extraction Technique for the Recognition of Segmented Handwritten Characters
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
A novel approach for structural feature extraction: contour vs. direction
Pattern Recognition Letters
A SVM-based cursive character recognizer
Pattern Recognition
Handwritten character recognition through two-stage foreground sub-sampling
Pattern Recognition
Off-line cursive script recognition: current advances, comparisons and remaining problems
Artificial Intelligence Review
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In this paper handwritten character recognition by human readers, a statistical classifier, and a neural network is compared to know the required accuracy for handwritten word recognition. Sample characters extracted from postal address words on mail pieces collected by USPS were used to evaluate human and machine performance. Experimental results show that 1) when the characters are segmented from words and are randomly presented, the accuracy of the machine recognition is comparable with the average human recognition accuracy, 2) the neural network employing the feature vector of size 64 outperforms the statistical classifier employing the same feature vector, and that 3) the statistical classifier employing the feature vector of size 400 achieves comparable recognition rate with the best human reader.