Abstract
Machine learning is currently playing a vital role in the next generation of the computer world. Automatic pattern recognition has become an important issue of image processing and machine learning. Handwritten digits and alphabets are not arranged in the same size, thickness, position and right direction. Therefore, to determine the issue of handwritten numerals and alphabet recognition, different classifications and complexity should be analyzed. The composition styles of different individuals affect mainly the patterns of alphabets and digits. An effective strategy is to understand the numbers and alphabets transferred from a document image and to make an orderly pattern. In this research work, a soft computing system has been developed using the MATLAB programming. This system uses machine learning algorithms that identify patterns by computerized estimation by identifying handwritten digits and alphabets from a document image. From the experimental results, we observed 96.24% average recognition accuracy of our proposed system.
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