# 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
Due to the high dependence of this approach on a large amount of writer-specific data and it needing multiple-pass decoding, which is time consuming, they proposed another generalized writer adaptation scheme in 2022 that can achieve fast sentence-level unsupervised adaptation [97]. Trained by identification loss (IDL), they proposed a style extractor network (SEN) consisting of a convolutional layer, a recurrent neural network with gated recurrent units (GRU), where the writing style is represented by a one-dimensional vector integrated by the output of the GRU. The style information extracted by the SEN is fed into the writer-independent recognizer to achieve adaptation. Thus, the performance of the recognizer using SEN is improved. Notably, in 2021, [PERSON] et al. [98] also proposed an intelligent residual attention gate module combining residual and attention frameworks based on fully convolutional neural networks. The module enhances the ability to extract meaningful features from text images by weighting to increase the importance of representative features and reduce the influence of background or noise, significantly improving the performance of the current HCTR. Different neural networks have different advantages and can be integrated by model merging. In recent years, RNNs and their modified networks have been used more often in combination with convolutional networks to recognize handwritten text. In 2019, [PERSON] et al. [99] proposed a neural network structure combining a Multi-level Multimodal Fusion module and an Attention-based LSTM module to fully use visual feature information and linguistic semantic information. [PERSON] et al. [100] proposed a Bidirectional Recurrent Neural Network (BiRNN) for text recognition. The network first obtains the semantic feature map by the convolution layer and then converts the semantic information into time series information through the conversion module and processes it through Bidirectional-LSTM. The results prove that the algorithm can analyze and predict characters by linking forward and backward contexts and effectively solve the problem of sample imbalance. In 2020, [PERSON] et al. [101] proposed a data enhancement method for HCTR and constructed a recognition network model combining CNN and LSTM (CNN-ResLSTM). The experiment proved that data preprocessing and data augmentation methods can effectively improve the model performance. In addition to the character encoding method discussed in [89], [PERSON] et al. [102] developed a new encoding technique called LOgographic DEComposition Encoding (LODEC). It can perform one-to-one mapping of Chinese. Instead of splitting characters into scattered stroke elements, the proposed encoding limits the decomposition to predefined radical components to encode many Chinese characters with a small number of basic elements. They also proposed a deep learning architecture called LODENet that extracts radical-based features from the input data, which is then decoded by a transformation network for radical-based features. In 2021, [PERSON] et al. [103] first proposed to use the RNN-Transducer model for offline HCTR. The network mainly comprises a visual feature encoder, language context encoder, and joint decoder. Specifically, CNN is used to obtain relevant visual features, and then the visual features are encoded according to the BLSTM module. On the other hand, relevant language context information is obtained through the Embedding layer and the LSTM module. Finally, the visual and language features are coupled and decoded into the final recognition result through the fully connected and softmax layers. In addition to the above approaches, the visual characteristics of glyphs and the semantics of Chinese characters are also exploited by [104]. In 2022, [PERSON] et al. proposed the Glyph-Semanteme fusion embedding (GSE) module according to the correlation between glyphs and semantics.