# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
###### Abstract  
Offline handwritten Chinese recognition is an important research area of pattern recognition, including offline handwritten Chinese character recognition (offline HCCR) and offline handwritten Chinese text recognition (offline HCTR), which are closely related to daily life. With new deep learning techniques and the combination with other domain knowledge, offline handwritten Chinese recognition has gained breakthroughs in methods and performance in recent years. However, there have yet to be articles that provide a technical review of this field since 2016. In light of this, this paper reviews the research progress and challenges of offline handwritten Chinese recognition based on traditional techniques, deep learning methods, methods combining deep learning with traditional techniques, and knowledge from other areas from 2016 to 2022. Firstly, it introduces the research background and status of handwritten Chinese recognition, standard datasets, and evaluation metrics. Secondly, a comprehensive summary and analysis of offline HCCR and offline HCTR approaches during the last seven years is provided, along with an explanation of their concepts, specifics, and performances. Finally, the main research problems in this field over the past few years are presented. The challenges still exist in offline handwritten Chinese recognition are discussed, aiming to inspire future research work. handwritten Chinese recognition; deep learning; convolutional neural network; recurrent neural network; long short-term memory; connectionist temporal classification +
Footnote †: journal: Medical  
the Chinese written on the electronic screen, such as the handwritten input of mobile phones, tablets, and other electronic devices. It records the timing information of Chinese writing and the relative position information of strokes [4]. Online handwritten Chinese recognition technology developed at the beginning of the twenty-first century with the rise of the electronic information industry. The technology and products related to printed Chinese recognition and online handwritten Chinese recognition have already matured. In addition, the object processed by offline handwritten Chinese recognition is the 2D picture of offline handwritten Chinese captured by a scanner or camera, which loses the timing information. Generally speaking, offline handwritten Chinese recognition is more complex than online handwritten Chinese recognition. At present, its recognition rate still cannot reach satisfactory results. Therefore, the recognition of offline handwritten Chinese remains the mainstream research direction. In offline handwritten Chinese recognition, depending on the number of Chinese characters contained in the input image, i.e., whether the task is to recognize single Chinese characters or lines of Chinese characters, Offline Handwritten Chinese Recognition can be further divided into Offline Handwritten Chinese Character Recognition (Offline HCCR) and Offline Handwritten Chinese Text Recognition (Offline HCTR). In the HCCR task, the grayscale image of a single handwritten Chinese character is studied and assigned to a corresponding category based on the information in the picture. A line or a paragraph of characters is included for text recognition. The entire section is recognized based on extracting individual characters in conjunction with contextual information. As shown in Figure 2, handwritten Chinese characters have more deformations in strokes than traditional printed Chinese characters and are less scalable for traditional algorithms such as template matching. Therefore, recognizing handwritten Chinese characters (HCC) and handwritten Chinese text (HCT) is currently more challenging. Specifically, the difficulties of offline handwritten Chinese recognition are mainly manifested in: (1) Compared with other languages, there are more types of Chinese characters.