Recognition of Handwritten Character using Recognition Model based on SVM

Handwritten character recognition (HCR) has been among the most exciting and demanding study aspects in image analysis and recognition of patterns. It makes a significant contribution to the procedure of autonomous advancement. The OCR-optical character recognition of the files has indeed been compared to handwritten files by a human. This OCR has been used to translate characters from numerous documents like images and word documents. The primary goal of this study is to offer an alternative for varied HCR methods like touch input from a portable display and image folder. It is challenging to train an HCR system under these preconditions effectively. Machine learning (ML) techniques have lately been the primary focus of HCR research, with impressive outcomes. Nevertheless, effective numeral recognition and classification remains a difficult task because of various writing patterns and blurriness, disruption, illumination, and dimension differences in the input collection. Thus, this study offers an effective and efficient HCR system to overcome these constraints, presenting a unique support vector machine (SVM). It has demonstrated quantitative SVM for OCR process performance, which provides a better outcome when modified with an ML technique. The suggested concept was evaluated in a training portion that comprised a variety of elegant characters and digits to learn with greater performance.