Quality evaluation methods of handwritten Chinese characters: a comprehensive survey

Abstract

Quality evaluation of handwritten Chinese characters aims to automatically quantify and assess handwritten Chinese characters through computer vision and machine learning technology. It is a topic of great concern for many handwriting learners and calligraphy enthusiasts. Over the past years, with the continuous development of computer technology, various new techniques have achieved flourishing and thriving progress. Nevertheless, how to realize fast and accurate character evaluation without human intervention is still one of the most challenging tasks in artificial intelligence. In this paper, we aim to provide a comprehensive survey of the existing handwritten Chinese character quality evaluation methods. Specifically, we first illustrate the research scope and background of the task. Then we outline our literature selection and analysis methodology, and review a series of related concepts, including common Chinese character features, evaluation metrics and classical machine learning models. After that, relying on the adopted mechanism and algorithm, we categorize the evaluation methods into two major groups: traditional methods and machine-learning-based methods. Representative approaches in each group are summarized, and their strengths and limitations are discussed in detail. Based on 191 papers in this survey, we finally conclude our paper with the challenges and future directions, with the expectation to provide some valuable illuminations for researchers in this field.