# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 7 Discussion
### OCR Deep-Learning Works
#### 7.1.3 GANs-Based Works [57; 58; 59; 60]  
The work of [PERSON] et al. [57], despite its excellent idea, remains vague in many aspects and is clouded in mystery. One of the unclear aspects of the results achieved in this work is whether they are based on character recognition or word recognition. Therefore it is better to report CER or WER, which are more representative of the accuracy of the OCR model than the accuracy alone. Moreover, if it is character-based, then the other metrics, such as recall, precision, and F-1 score, should demonstrate a better understanding of the results. Furthermore, it is also not clear whether the classes were somehow balanced after they were augmented with the generated images. High accuracy alone is not sufficient to determine this. In addition, the number of images after the augmentation using GAN was not mentioned. Furthermore, the number of parameters was not reported. Additionally, since the part used for the testing was only a subset of that used for the training, high accuracy was expected. Nevertheless, the split amount between the training and the testing was still mysterious. Overall, further investigation is required to determine whether these points are signs of the overfitting of the model or of a model that genuinely generalizes well. [PERSON] et al. [58] missed the details of the adaptive augmentation technique, such as the algorithm itself and the basis or formula that determines the number of characters to be cut off and scaled within a certain range, as in the IFN/ENIT dataset, most characters become 100K occurrences, while in the AHDB dataset, they become approximately 43K occurrences. Moreover, it is not clear whether the different splits for testing (d, e, and f) were made purely stochastically or according to some similar features, and further clarification is needed as to why set d performed better than set e and why set e performed better than set f. It is possible that there are sets that have noticeably more characters than others, which would explain the diversity in the levels of accuracy. In addition, when test sets d and e were combined in training, the test on set f seemed to have the lowest performance, which may indicate that set f was the most challenging set. However, the authors neither discussed this point nor trained the model on set f, and they tested it on combinations of sets e and d.  
There are some limitations to [PERSON] et al. [59]'s approach. First, in most cases, letters are connected in Arabic rather than being isolated, and these cases were excluded. Second, ensuring the quality of the generated samples of letters containing dots remains a challenge, as this level of quality has not yet been reached. Third, neither was the dataset used for generating the samples balanced, nor was the split between the training and testing that led to high accuracy reasonable. Fourth, since the ML model of classification is under assessment, the accuracy can mostly be a misrepresentation rather than a true representation of the model's performance; therefore, other metrics, such as recall, F-1 score, and precision must be used. A remarkable effort and empirical analysis were applied in the work of [PERSON] et al. [60], on a challenging task. Nevertheless, there are some limitations to this approach. The first is the complexity involved in using several combinations of deep-learning models in each step. The second is that the accuracy of the Arabic recognizer, despite the use of all these complex models, is still very close to that of the nearest baseline model, which suggests that more attention should be paid to the OCR and post-processing steps, rather than focusing solely on binarization as a step in pre-processing. The third is that the quality of the images generated by generative adversarial networks is better represented using Frechet inception distance (FID), but this was not used. Moreover, the training and testing splits were ambiguous.