Handwritten Digits Recognition Based on Deep Learning4j

Over the past few decades, Optical Character Recognition (OCR), particularly handwriting recognition, has received much attention. Handwritten Digits Recognition (HDR) means, receive and comprehend handwriting inputs from different sources for example pictures, touch screens, paper documents, and other devices. The field of HDR has witnessed rapid progress owing to the concurrent availability of cheap and well-assembled computers, advancements in learning algorithms, and availability of large databases. In recent years, HDR has received much attention due to ambiguity in learning methods. The aim of the current study was to explore the potential of Deeplearnig4j (DL4J) framework for HDR. DL4J offers the most appropriate framework for the identification of handwritten digits. To execute the task of HDR, Convolutional Neural Network (CNN) is implemented. This study measures the strength and productivity of DL4J for the aforementioned tasks of recognition and attempts to upgrade the procedure. Results obtained shows significant improvement in the recognition rates of hand-typed digits. Though the accuracy and error rates obtained through our proposed system (CNN-DL4J) show variations, on average the accuracy rate remained at 97 %. The aim of the proposed endeavor was to make the path towards digitalization clearer. Though the purpose was only to identify the digits, we can extend it to deal with digits having different sizes, different languages (Urdu, Arabic, Persian), letters, and the task of detecting multidigit person's handwriting. Hence, it could reduce the typing need to an extent that people will be able to convert their handwritten materials into digital form in one click on its picture. Altogether, this investigation combines CNN with the DL4J framework and took MNIST as a standard dataset to accomplish the task of digit recognition. In addition, the test framework can be assessed in the future for the prospects of image classification and such other pattern recognition tasks.