Character Recognition Techniques using Machine Learning: A Comprehensive Study

Optical Character Recognition (OCR) technology is essential in converting printed or handwritten documents into machine-readable text. This paper provides an overview of machine learning based OCR technology, including its history, principles, and applications. The paper discusses the challenges and limitations of machine learning based OCR and presents various OCR models based on different feature extraction and classification methods. Specifically, it covers feature extraction methods such as Euclidean distance, HOG, Fourier descriptor, and directional chain code, which are used to improve the accuracy of classification models such as CNN, KNN, ANN, and SVM. The paper also discusses recent advancements in OCR for different languages and how OCR can be integrated with machine learning and AI to understand ancient scripts and convert them to modern languages. The paper concludes with a discussion on the future of multi-lingual OCR and integration with AI and machine learning models, which have the potential to revolutionize the field of OCR. In summary, this paper provides a comprehensive overview of OCR technology, its advancements in various languages and potential for future development.