Avoiding Segmentation in Multi-Digit Numeral String Recognition by Combining Single and Two-Digit Classifiers Trained without Negative Examples
- Dan C. Ciresan
- 2008 10th International Symposium on Symbolic and…
In this paper, a framework for off-line handwritten numeral string recognition based on stroke grouping is proposed. In our approach, strokes are aligned into a sequence of strokes and then segmentation process is performed to partition strokes in the sequence into possible-digits, that is, groups of strokes which may be possibly a digit. As the result of stroke grouping, grouping-hypotheses, which imply possible segmentation, are generated. An input numeral string is recognized by dynamic programming scheme, in which the best grouping-hypothesis with maximum matching score is chosen. The framework also provides systematic way of reducing computational complexity by embedding external knowledge into the framework. The experimental results to evaluate the proposed framework are shown.