# A Pipeline Approach to Context-Aware Handwritten Text Recognition
## 2 Literature Review  
The emergence of deep learning (DL) has brought significant advances to HTR. The DL models have progressively improved the performance of HTR transcription over the years. This section discusses the different ideas proposed to solve HTR and the performance achieved. [PERSON] presented an approach for joint line segmentation for HTR transcription [11]. The dataset was taken from paragraph-level images from the RIMES and IAM datasets. The author proposed the integration of the attention mechanism with the Multi-dimensional Long Short-Term Memory Recurrent Neural Network (MDLSTM-RNN) for implicit text segmentation and transcription. The collapse layer of the model was modified with an attention mechanism to provide the weights in identifying the input positions on a paragraph image iteratively to enable a free segmentation method for text transcription. As a result, the model achieved a CER of 4.9 and 2.5 on the IAM and RIMES datasets, respectively, containing images with 300 dpi resolution. [PERSON] et al. proposed a model of Start, Follow, and Read (SFR) for historical HTR [12]. The SFR model could identify the text position by using a Region Proposal Network and a CNN-LSTM model for text transcription. The proposed SFR model was composed of a Start-of-Line (SOL) finder, which identified the text line of a given image, a Line Follower (LF), which segmented the position identified by the SOL finder iteratively and, lastly, the HTR model. The 2017 ICDAR full-page competition dataset was applied in the study of German handwriting. The proposed model achieved outstanding performance, with a BLEU score of 72.3. The model was also evaluated on the RIMES and IAM datasets, achieving a CER and WER of 2.1 and 9.3, as well as 6.4 and 23.2, respectively. There was another study that applied CNN to a Kannada handwritten document [13]. In the paper, the author proposed CNN for training the data. In the experiment setup, the Chars74K dataset containing over 657 classes was applied. Each class has 25 handwritten characters. Data augmentation techniques, including denoising, contrast normalization, segmentation, gray-scale conversion, and binarization, were performed to expand the dataset size. After a hundred epochs of training, the model achieved 99% accuracy on the Chars74k dataset and 96% on a self-collected handwritten document. [PERSON] et al. conducted a study for a scalable HTR system [3]. The authors integrated the proposed HTR system into a larger-scale OCR system. In the study, the authors applied LSTM-based models and gated recurrent convolutional layers (GRCL) as a fully feed-forward network model for line-level text recognition and classification. Online handwritten data from the IAM offline and online databases and a self-collected online handwritten sample were used. The authors trained separate models for both printed and handwritten words. The dataset used for both printed and handwritten text consisted of 508 and 433 images. After hyperparameter tuning, the proposed GRCL achieved a character error rate (CER) and word error rate (WER) of 4.0 and 10.8, respectively. [PERSON] et al. presented a method to detect and recognize handwritten text and text-line [14]. The authors presented a method named Multi-Level Convolutions Convolutional and Recurrent Network (MLC-CRNN), which combined different deep learning techniques, including CNN, RNN, and Connectionist Temporal Classification (CTC) loss function. In the paper, the Connectionist Text Proposal Network (CTPN) was used in training a new model for a handwriting text-line detector. Following handwriting text recognition, the team applied a refined CRNN model. To make it a multi-layer convolution (MLC), two more branches were added linearly on the original convolutional layers. The datasets used for training were obtained from three hundred students. The participants were asked to write on a standard answer sheet. The training set contained 3883 images, and the testing set contained 297 images.