Abstract
A fuzzy system for handwritten numerals recognition using a fuzzy Hough transform technique is presented. The system is an off-line system since the data processed was written before the time of recognition. A data base of 480 patterns of unconstrained (free) handwritten numerals was used in the proposed system. Membership values are determined as fuzzy sets which are defined on the standard Hough transform vector. Manhattan distance measurement has been used to measure the similarity of an input feature vector to a number of numeral pattern classes. The overall recognition accuracy of the system for the ten numerals is 95%.
Highlights
People usually write numbers in different shapes
The present numeral recognition system is compared to other Indian character recognition systems
We can claim that the character recognition rate achieved for numerals is 92.2%, which is among the best reported
Summary
People usually write numbers in different shapes. Recognition of handwriting has many practical applications, including mail address and bank check reading. As in any pattern recognition problem, different approaches to numerals recognition can be classified into two classes: the decision theoretic approach and the structural approach [1]. The decision theoretic approach includes many techniques, such as minimum distance classifier (which is used in this research), matching by correlation, optimum statistical classifier, and multilayer neural networks. In these approaches the recognition is based on the use of a decision function to separate different classes of patterns. These decision theoretic approaches ignore any structural relationship in a pattern's shape. The syntactic approach is the most famous approach of the structural class [2]
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