Application and analysis of machine learning in handwritten digit recognition

Abstract

It appears from the information that Character recognition research is currently focused on handwritten digit recognition, a significant subfield of optical character recognition, i.e. the use of computers to recognise and process digital information. In today's increasingly mainstream computer and data era, handwritten numeric recognition can simplify the process of paper-based offices, reduce the intensity of work when analysing data statistics afterwards and improve work efficiency. There are many algorithms to achieve recognition, each with different recognition accuracy, implementation efficiency and application scope. Based on the basic concepts described above, this thesis investigates the efficiency and accuracy of three algorithms - template matching, SVM and deep learning - in recognising handwritten digits with different sample sizes. The models or kernel functions currently used to process data of varying complexity and restricted scenarios also require continuous improvement to ensure accuracy, making it all the more important to discuss them in detail.