# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 4 Handwritten Chinese Text Recognition
### Line-Level Text Recognition
#### 4.1.2 Segmentation-Free Recognition  
The segmentation-free recognition method focuses on continuous feature extraction of the input text images. The possible sequences of characters represented in the sequential features are then matched with those of single characters. The best-matched text sequence is derived as the output of the whole recognition according to the evaluation criteria of the objective function. The HMM based on the Gaussian Mixture Model (GMM) proposed by  
Figure 9: HCTR algorithm based on weakly-supervised learning [77]. [PERSON] et al. [81] became the representative algorithm in handwritten text recognition. As the length of recognized characters increases, the HMM-based approach generates too many parameters that limit the recognition performance. Subsequently, offline handwritten text recognition based on or combined with neural networks has evolved more significantly. In recent years, some approaches using the Multi-dimensional LSTM (MDLSTM) Model [82], Convolutional Recurrent Neural Network (CRNN) [83], Connectionist Temporal Classification (CTC) algorithm [84], and attention mechanism [85], etc., have improved the model recognition accuracy to different degrees. To apply MDLSTM-RNN to HCTR with an accuracy of 83.5% was first proposed by [PERSON] et al. [86]. [PERSON] et al. [87] pointed out that the current data augmentation method can significantly improve the accuracy of the recognition task of printed documents. However, it is not suitable for the task of handwriting recognition. Therefore, the team utilizes the baseline model MDLSTM-RNN [88] for generating synthetic line images. By training and testing on the CASIA dataset, the character error rate of the model trained with synthetic images and real images is reduced by 10.4%. [PERSON] et al. [89] proposed using character decomposition techniques to speed up HCTR for the slow system of [86]. Their system consists of an optical model based on MDLSTM-RNN and a language model. They use Cangjie [90], Vubi [91], and an arbitrary encoding to decompose characters for experiments without significant modification to the baseline MDLSTM-RNN. [PERSON] et al. [92] improved the MDLSTM-RNN network. In addition, the network based on SMDLSTM-RNN applies fewer MDLSTM layers than the network based on MDLSTM-RNN [86], consumes much fewer resources and computation, and improves the accuracy by 3.14% without adding a corpus. [PERSON] et al. conducted a series of studies combining neural networks with HMM. In 2016, they proposed a deep neural network (DNN-HMM) based on the Bayesian decision for handwritten text recognition [93]. The model extracts the gradient features based on the classifier of DNN, and then the HMM models the text lines sequentially. Finally, the feature language model is integrated with the DNN-HMM feature model, and the final recognition result can be obtained by Bayesian decision. Later, they extended their work in [94] to investigate the key issues of feature extraction, character modeling, and language modeling when using GMM-HMM, DNN-HMM, and DCNN-HMM to implement HCTR. They proposed a hybrid neural network Hidden Markov Model (NN-HMM) and proved its effectiveness for offline HCTR [95]. In 2020, they proposed a Chinese text recognition method (CNN-PHMM) that combines a writer-aware CNN network and parsimonious HMM [96]. First, the state binding algorithm is used by PHMM to reduce the total amount of HMM states. Second, WCNN integrates each convolutional layer with an adaptive layer composed of writer-dependent vectors and optimizes it by combining it with other network parameters to obtain the final recognition result. Though the method segments characters, their approach reduces the CER for recognition of the ICDAR 2013 dataset to the best result at that time based on the compact design of the output layers and the writer-aware convolutional layers.