# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 4 Handwritten Chinese Text Recognition
### Line-Level Text Recognition
#### 4.1.1 Segmentation-Based Recognition  
The segmentation-based text recognition approach is based on individual Chinese character recognition. Both character shape modeling and linguistic context modeling play a significant role. Most of the previous segmentation-based approaches are based on over-segmentation strategies. This approach usually involves building several modules, including character over-segmentation, character classification, language modeling, and geometric modeling. Then, they are integrated to find the optimal path. In 2016, [PERSON] et al. [75] used deep knowledge containing character or non-character labels, class labels, and the heterogeneous CNN that can be trained by deep knowledge to recognize HCT. The text will be over-segmented, so the HCT deep knowledge contains two parts: segmentation labels and classification labels for each segmented character. They proposed two types of heterogeneous CNN models, the cascading CNN containing 2-class CNN and all-class CNN, and the negative-awareness CNN. The proposed method has the advantage of incorporating the language model deeper into the path search when combined with language models compared to Long Short-Term Memory (LSTM) and Hidden Markov Model (HMM) frameworks that are good at handling time sequence tasks. Subsequently, during the period of back-off N-gram language models (BLMs) as the standard language model for the HCT task, [PERSON] et al. [76] first tried to recognize HCT using the neural network language model (NNLM) based on over-segmented text. They use CNN-based neural networks as a shape model for text recognition, which is used to accomplish over-segmentation of characters, classification, and as a geometric context model, and combined with hybrid NNLMs to improve the performance of the recognition system. Their approach achieved the best recognition result then and is often used as a comparison for the recognition results of other research methods on HCTR afterward. The research approaches since then have been focused on segmentation-free text recognition. In 2020, [PERSON] et al. [77] proposed an HCTR method based on weakly supervised learning, as shown in Figure 9. Firstly, the text image is segmented by a CNN-based segmentation algorithm to generate a series of original text segments. Then, it is combined into one or more consecutive text segments to form a corresponding candidate character pattern and form a character candidate region. Finally, the wave beam search method is used to search the optimal path in the candidate region. The language and character classification models are combined to obtain the final recognition result. Their final experimental results show the competitive recognition performance of the method compared with other state-of-the-art methods. There is also room to further improve the performance by combining more powerful language models. Unlike over-segmentation-based methods, [PERSON] et al. [78; 79] used the full convolutional network-based approach for character detection as character segmentation, providing a new idea for segmentation-based HCTR. They proposed a fully convolutional network (FCN) that enables fast and accurate implementation of HCTR [78]. The structure extends three modules for detecting, localization, and classifying characters in text after a backbone network generates feature maps. Their proposed network model not only produces recognition results compared to the mainstream approach Recurrent Neural Network (RNN)/LSTM but also contains segmentation results that can be used for other subsequent applications, such as removing certain information. Compared with over-segmentation-based methods such as [75], this method can somewhat solve the problem of difficulty in segmenting characters due to overlapping characters, etc. Still, the recognition efficiency is not very satisfactory compared to other methods. In 2022, they further proposed a segmentation-annotation-free approach to segment and recognize text [79].