# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 7 Discussion  
### OCR Deep-Learning Works  
#### 7.1.1 CNN-LSTM-CTC Based-Works [47; 48; 49; 50; 51; 52; 53]  
[PERSON] [47] conducted the testing of model accuracy on a small AHTID dataset, testing 901 samples. Testing on more datasets would produce a more realistic evaluation of the model. [PERSON] et al. [48] tested the model on a set that was split from the same augmented set for training. The production of such high accuracy is not a strange phenomenon; however, it may indicate an overfitted model, since no real testing of generalization was performed. Therefore, the authors could use the other unused parts of the IFN/ENIT dataset to perform much more robust testing of their model. Moreover, they reversed the reporting of the WER to 91.79% rather than 8.21%, which may lead to some confusion. [PERSON] et al. [49] used a complex architecture that has the drawback of requiring significant time to train due to the additional layers of BiLSTM. [PERSON] et al. [50] reported accuracy as part of the confusion matrix, which is not an appropriate indicator of the accurate performance of the model in the case of OCR. The appropriate method with which to measure it is the use of the character-error rate or word-error rate, which the authors did not specify. Moreover, such high accuracy in a testing set can indicate that the set used for testing is identical to the training and not sufficiently challenging e for the model. Therefore, it was not demonstrated whether the results can be generalized, since the details of the part of the IFN/ENIT dataset on which the testing was conducted were missing and examined blindly. [PERSON] et al. [51] achieved 98% accuracy. It is no surprise that this level of accuracy was achieved. The splitting of the dataset into 6000 instances for training and only 1000 for testing meant a ratio of 85% for training and 15% for testing, which resulted in high accuracy in both the training and testing sets. Moreover, this indicated that there may have been an overfit, since the testing was performed only on a small part of the dataset. In addition, the validation-set amount was not reported; rather, it was mentioned that it was only a small subset of the training set. Overall, a better judgment of the accuracy of the model could have been made if more portions had been given to the testing set and if the validation part had been appropriately determined. Furthermore, the real indicator of the accuracy of an OCR model is not the accuracy itself but, rather, the character- or word-error rate; however, if the model focuses on a single character's classification each time, then the use of the accuracy metric is justifiable. Therefore, for example, if a sequence of numbers is fed to this model to be tested, the accuracy metric does not represent the real accuracy of the model. [PERSON] et al. [52] achieved a word-recognition rate that differed drastically from the character-recognition rate. This is an open issue to be resolved further by implementing post-processing techniques. [PERSON] et al. [53] achieved, with their best model, a word-recognition rate for normalized text of 58%. This sheds light on the common issue of low WRR, suggesting the need for extra steps to post-process the resultant text. In addition, it is true that they tested the test set on different platforms, but more variations of the test set itself could have made for a detailed empirical analysis. [PERSON] [54] used only 5% of their training dataset for validation, while 95% was used for training on the merged training set, which may have been a sign of overfitting. It can be concluded from the works critiqued above that:  
* CNN-LSTM-CTC works are tested on test sets that have no fair split in proportion to the training sets or are tested on sets that are not sufficiently challenging. Therefore, high accuracies could be a sign of overfitting. This issue can be fixed by training with an appropriate proportion between the training and testing splits.