Structural model constructing for optical handwritten character recognition

Abstract

Abstract. The article is devoted to the development of the algorithms for optical handwritten character recognition based on the structural models constructing. The main advantage of these algorithms is the low requirement regarding the number of reference images. The one-pass approach to a thinning of the binary character representation has been proposed. This approach is based on the joint use of Zhang-Suen and Wu-Tsai algorithms. The effectiveness of the proposed approach is confirmed by the results of the experiments. The article includes the detailed description of the structural model constructing algorithm's steps. The proposed algorithm has been implemented in character processing application and has been approved on MNIST handwriting characters database. Algorithms that could be used in case of limited reference images number were used for the comparison.

Research on Artificial Intelligence Machine Learning Character Recognition Algorithm Based on Feature Fusion

“…Literature [3] proposed VGG network, Literature [4] proposed GoogleNet, Literature [5] proposed typical deep convolution neural networks. Literature [6] applied deep learning algorithm in the field of character recognition, which caused the whole world to conduct in-depth research on it. The research results provide a new way of thinking for realizing Chinese character recognition and blind reading, and it is a research direction with potential application prospect.…”