# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 3 Handwritten Chinese Character Recognition
### Recognition Methods Based on Traditional Technology  
The first to break the barrier of the HCCR task was a recognition model for handwritten Japanese and Chinese characters designed by [PERSON] et al. in 1981 [PERSON] et al. (1981). Chinese researchers started to study HCCR in depth after 1980, and in late 1989, Tsinghua University successfully developed a system that could recognize 3755 frequently used Chinese characters on an enormous scale [PERSON] et al. (1981). A few years later, in 1997, the THOCR-97 integrated Chinese character recognition system, also designed by Tsinghua University, achieved an accuracy rate of 95.8% while limiting the neatness of the writing [PERSON] et al. (1981). The traditional HCCR method is mainly divided into three steps: pre-processing the image, extracting the features with distinguishing ability, and recognizing according to specific classification rules. The recognition process and methods commonly used in each step are shown in Figure 3.  
Figure 3: Traditional offline HCCR process and typical methods for each step. The orange rectangle shows some features extracted with traditional techniques and a few commonly used machine learning classifiers. Image pre-processing directly affects the effect of subsequent feature extraction and recognition classification. The information on handwritten Chinese characters in real life is not ideally standardized, neat, and transparent. Therefore, we need to discard information irrelevant to recognition when we perform effective information extraction. Typical pre-processing operations include binarization, smooth denoising, and sample normalization. The image binarization process transforms the whole character image into only black and white. Since the background of the handwritten Chinese character image is relatively simple, this method can clearly extract the target character from the background and noise information. Normalization operation is to normalize images of different scales to the same scale without changing the aspect ratio to eliminate the effect of scale differences. Feature extraction is a critical step in traditional Chinese character recognition. One of the mainstream methods is based on structural features, which extract features mainly by analyzing Chinese characters' composition structure, strokes, and radicals. The other one is based on statistical features. Due to structural feature extraction's difficulty and noise sensitivity, statistical-based feature extraction methods show better performance. For example, HOG features [20], Gabor features [21], ICA features [22], and Gradient features [23] have obtained high recognition rates in the HCCR task. In terms of classifier selection, the commonly used models are K-Nearest Neighbor (KNN) [24], Bayes Classifier [25], Support Vector Machine (SVM) [26], Quadratic Discriminant Function (QDF) [27], and Modified Quadratic Discriminant Function (MQDF) [28]. Among them, SVM is a fast and efficient method for classifying small sample sets. SVM has strong robustness and generalization ability, but it is more sensitive to missing data, and its efficiency decays faster when there are too many data samples. The classification efficiency of the naive Bayesian classifier is more stable, more tolerant to missing data, and more robust, but the requirement of data independence is very high, and the sample data often do not meet such requirements in practice. Hence, the classification prediction effect is not very satisfactory. In contrast, the MQDF classifier based on Gradient features has achieved better results in recognition tasks and has been widely used in practice. With the rise of deep learning, little research has been carried out in the past seven years to do offline HCCR tasks based on the traditional \"pre-processing + feature extraction + classification\" framework. In 2016, [PERSON] et al. [29] proposed a method to recognize handwritten Chinese characters by combining the substructures of Chinese characters.