On the Recognition of Printed Characters of Any Font and Size
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of handwritten characters—a review
Image and Vision Computing
A preprocessing algorithm for handwritten character recognition
Pattern Recognition Letters
A high accuracy algorithm for recognition of handwritten numerals
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Chain Pyramid: Hierarchical Contour Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Character recognition—a review
Pattern Recognition
Decomposition of gray-scale morphological structuring elements
Pattern Recognition
Hierarchical Image Analysis Using Irregular Tessellations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Lower Bound for Structuring Element Decompositions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decomposition of Convex Polygonal Morphological Structuring Elements into Neighborhood Subsets
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Iterative Growing and Pruning Algorithm for Classification Tree Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Contour Decomposition Using a Constant Curvature Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwritten numerical recognition based on multiple algorithms
Pattern Recognition
Building a new generation of handwriting recognition systems
Pattern Recognition Letters - Postal processing and character recognition
Document image analysis: a bibliography
Machine Vision and Applications - Special issue: document image analysis techniques
Digital Pattern Recognition
Recognition of handwritten characters by parts with multiple orientations
Mathematical and Computer Modelling: An International Journal
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A combining approach has been studied previously to integrate different parts of handwritten characters for their analysis and recognition. Perfect combinations, by which the characters can be identified with certainty, are important to pattern analysis and character recognition. However, a large number of possible combinations (e.g., 63 combinations for a character partitioned into six parts), also produce a lot of perfect combinations. Hence, it is necessary to determine which of them are most important. In this paper, we propose a methodology of finding the basic crucial combinations, and algorithms to compute them. Compared with perfect combinations, such basic crucial combinations are most significant to the character distinctiveness. Similarly, the largest confusion regions are also identified. Experimental studies have also been conducted using the 89 most frequently used patterns of 36 alphanumeric handprints, to obtain their basic crucial combinations and largest confusion combinations. The results indicate that the ratio of the number of basic crucial combinations to perfect combinations is only 12.6%, and the ratio of the number of the largest confusion regions to the total confusion combinations is 15.6%.