# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 6 Analysis of Recent Works
### OCR Deep-Learning Works  
#### 6.1.1 CNN-LSTM-CTC Based-Works [47; 48; 49; 50; 51; 52; 53]  
[PERSON] [47] presented a technique using transfer learning based on the CNN-BLSTM-CTC architecture. The model was first trained on four fonts (Tahoma, Thuluth, Andalus, and Naskh) of printed text from the P-KHATT dataset using 25,888 pictures, and then validated on 5656; therefore, the parameters were learned. Next, the model was retrained on the KHATT (9494 samples) and AHTID (2699 samples) datasets of Arabic handwriting. The testing on KHATT achieved a CER of 2.74% and a WER of 17.84%. On AHTID, 2.03% CER and 15.85% WER are achieved. [PERSON] et al. [48] used five layers of CNN followed by two RNN layers and then a CTC layer to compute the loss between original text and the text decoded from pictures by taking the most probable character in each time step and eliminating the duplicate characters and blanks. They used the IFN/ENIT dataset, particularly 946 names out of 32,492 handwritten names of Tunisian villages written by more than 1000 writers. To augment the images in the dataset, they applied rotations, shift-ups, and shearing-ups to generate 15 examples for each image, ending up with 487,350 training samples, which  
Figure 9: Taxonomy of the analyzed works. equals roughly 3,876,648 characters. The resulting WER and CER were 8.21% and 2.10%, respectively, for the best train-test split of 80-20. [PERSON] et al. [49] proposed a model based on CRNN-BiLSTM to recognize Arabic handwritten text. The dataset used to accurately train the model was MADCAT. This model allows simultaneous text detection and segmentation. It achieved high accuracy in parsing the page completely, segmenting it line-wise, and then predicting what would be written. A large-scale dataset of Arabic handwritten documents that is challenging for OCR was used to evaluate the model. The character-error rate was 3.96%. [PERSON] et al. [50] created a custom dataset of handwritten Arabic text as words constituted by weekday names, called the Arabic Handwritten Weekdays Dataset (AHWD). It was written by 1000 people and has 21,357 words uniformly distributed between the seven days. The researchers further utilized DCNN for training and testing on this dataset. Their aim was to make the model generalize to new datasets as well. Their proposed model starts by training a DCNN on a balanced and randomly selected dataset using different structures. In order to enhance the results and avoid overfitting, proper drop-out, image regularization, and the learning rate were used. Lastly, blind testing was performed on the hidden set of tests. The results showed that the model's accuracy was promising, achieving 0.9939 accuracy and a 0.0461 error rate on the AHWD dataset and, on the IFN/ENIT dataset, a 0.9971 accuracy rate and a 0.0171 error rate. [PERSON] et al. [51] proposed a model to recognize Arabic handwritten digits by employing pre-training on a CNN-LSTM architecture. The former part (VGG-16 or LeNet) is responsible for extracting the representative visual features, while the latter (LSTM) holds the long-term dependencies between the extracted features in the previous step. The datasets used for the training and testing were AHDBase and MAHDBase. They were augmented using different transformation techniques, such as shifting, shearing, and zooming. For the training, 6K images were used, and 1K images were used for the testing. To evaluate the classification model, training and validation loss, accuracy, recall, and precision were reported. In the AHDBase dataset, LeNet + LSTM scored 99% for recall and precision, and VGG-16 + LSTM reached a 98.92% accuracy. In MAHDBase, both LeNet + LSTM and VGG-17 + LSTM scored 99% for recall and precision, and LeNet + LSTM reached a 98.47% accuracy. [PERSON] et al. [52] proposed a CNN-BiLSTM model with CTC loss to recognize printed Arabic text without character segmentation. The five layers of CNN worked as feature extractors, while the two layers of BiLSTM were used for long-term dependencies.