A NEURAL NETWORK METHOD TO PERFORM OPTICAL CHARACTER RECOGNITION OF HANDWRITTEN CHARACTER

Abstract

The machine learning concept presents a challenge to the designer to fully utilize machine capacity. Machine learning utilizes knowledge of recent data on a specific subject to extract hidden information that is contained in the data. We can achieve machine learning and predict results for unpredicted data by using specific mathematical functions and concepts to uncover hidden information. One of the key uses for ML is pattern recognition. the large picture usually data sets are deployed to recognize patterns. An example of pattern recognition through an image is handwriting recognition. We may teach computers to interpret letters and numbers from any language that is contained in an image by employing such notions. Several techniques exist for identifying handwritten characters. Given its wide range of applications, handwriting recognition has drawn considerable interest in the domains of pattern recognition and machine learning. Both handwritten and optical character recognition (HCR) have distinct fields of use.