# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 7 Discussion
### Robust OCR and Text-Correction Techniques
However, semantic errors, style problems, and sentence-level problems were neglected. In light of the amount of data and error types, this work's high results suggest that further robust results can be achieved by including more training data and error types. [PERSON] [66] utilized SkipGram, FastText, and BERT on the ANC-KACST dataset. Despite the fact that the test set is different for each model and that the recall was reported at 88.9, the study shows the effectiveness of combining embedding techniques to detect and correct Arabic semantic errors, which can be used in the future to build more robust models. [PERSON] et al. [71] proposed a DBN with three RBM layers and borderline-SMOTE for balancing classes by over-sampling. The used datasets are Tashkeela and LDC ATB3; 2.21 CER and 6.73 WER were reported. This model provides robust results in the same datasets but takes an excessive amount of time to train, and the quality of the results drops if small batch sizes are used. Despite the fact that development and experimentation are required, it is suggested that this model can be made more robust by enhancing its performance and utilizing different embeddings and datasets. It was noticed that since research is currently ongoing on both OCR techniques and text-correction techniques, and that significant progress remains to be achieved in datasets, models, and their testing, we cannot guarantee universally robust models in adverse settings, such as difficult and unclear paper conditions, difficult text, and the presence of rare fonts. However, given the previously achieved results and based on the amount and quality of data and models, a pattern is noticeable in which CNN-LSTM-CTC OCR models achieve robust results even if few data are given, and that Transformer is a potential robust OCR architecture for the future, once more computational power and diverse data in large volumes are made available. In text correction, there is a pattern that suggests the enhancement of accuracy by utilizing text-embedding techniques that eventually become robust. Nevertheless, it is necessary for practitioners to perform more dataset creation and experimentation with different embeddings and models. To summarize the discussion, we first discussed each work individually and critically, and then concluded the findings for each technique in OCR and in works on deep-learning-enabled text correction. For OCR, the combination of CNN, LSTM, and CTC is the formula that allows good results, even if few data are available. If such a condition is satisfied--the availability of high amounts of data--then Transformer is the recommended architecture for the future. Nevertheless, more datasets are needed for Arabic that are domain-specific and of high quality. One way to deal with the scarcity of datasets is to augment them using GANs, but research should produce more datasets to ensure progress in this field. For text correction, common problems include the lack of error types and their uneven distribution. Moreover, models still suffer from challenging problems involving dialects and punctuation, and context-aware datasets have not been developed. In essence, embedding techniques such as BERT and FastText show improvements in accuracy and should be explored further in Arabic. Third, by investigating robust models, we conclude that no universal model is sufficiently robust for all difficult paper, text, and font types. Nevertheless, we can observe that under the current conditions, CNN-LSTM-CTC gives robust results, even if no large datasets are accessible, and Transformer has the potential to give better results if large data and computational sources are made available. On the other hand, techniques including text embedding show robust results, but further experiments and datasets are needed for more robust techniques. Overall, for both OCR and text correction, significant focus is to be placed on the creation of more challenging and high-quality datasets and the application of advanced models, with appropriate practices followed in model testing.