# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 7 Discussion
### Post-OCR-Correction Works [61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71]  
One significant limitation of the work of [PERSON] et al. [61] is that the corpus building has an obviously uneven distribution between the types and categories of error; therefore, a lack of accuracy and results is associated with low-frequency errors. Evidence of this is shown as the numbers of deletion and keyboard errors (which obtained the worst recall, at 0.337 and 0.644, respectively) were, in fact, among the lowest in the dataset, comprising 0.67% and 6.26%, respectively. A remedy for this observed fact is to augment with more data from the scarcest parts and cut off from the dominant part until they become balanced; consequently, such imbalances in the dataset are ameliorated and, therefore, the overall  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Work** & **Year** & **Technique** & **Dataset** & **CER** & **WER** & **Description** & **Limitation(s)** \\ \hline \multirow{6}{*}{[PERSON] et al. [55]} & \multirow{6}{*}{2021} & CNN-Transformer & Custom dataset, KHATT & \multirow{6}{*}{7.27} & \multirow{6}{*}{8.29} & Resnet101 with 101 layers and \multirow{6}{*}{} & \multirow{6}{*}{Shortage of resources} \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \multirow{6}{*}{[PERSON]. [PERSON] et al. [56]} & \multirow{6}{*}{2022} & Transformer with cross-attention & Custom dataset, KHATT & \multirow{6}{*}{18.45} & \multirow{6}{*}{N/A} & DeIT-base and asafaya-BERT-base-arabic were used to initialize the model, which had twelve attention heads for encoder and eight for decoder and eight leading to a limited CER} \\  & & & & & & & \\  & & & & & & & \\  & & & & & & &  
performance can be improved. Another limitation is that since the parts of the dataset that are used for testing are not specified and it can be assumed that the testing set also consists of the same proportions as the training set, the dominant parts of the dataset can cause the testing set to achieve high results. Therefore, the testing set should have a uniform number of error categories. [PERSON] et al. [62] presented work that was limited to experiments with word-form, verb-tense, and noun-number errors. Many error types were neglected, such as semantic errors, style problems, sentence-level problems, and incorrect ordering. An F-measure of 93.89% was achieved in comparison with other systems, and the intra-attention mechanism improved the recall by 4.28, although it failed in some cases; therefore, attention may be paid to other error types to achieve better intra-sentence attention. [PERSON] et al. [63] proposed a model that still suffers when faced with challenging examples, as it can fail to correct missing words. Furthermore, it is still weak in correcting dialect words and suffers from errors in the correction of punctuation. Further efforts involving different architectures and a greater focus on dialect-word mistakes, would be beneficial. Moreover, the computation and training time are still noticeably high. [PERSON] et al. [64] used a test set that was excessively small compared with the training sets, as it consisted of 2443 words, which is 0.10% of the Tashkeela set's word count. [PERSON] et al. [65] conducted testing only on a very small portion, of 0.55%, of the total data. Furthermore, their testing lacked the ability to correct dialectic and punctuation errors. [PERSON] [66] was not able to compile a fair comparison with other models, as the test set was different, albeit derived from the same dataset. [PERSON] [67] did not report F1 or recall. In addition, the model takes an excessive amount of time to train (11 h), while Transformer achieves comparable results in less time (3 h). [PERSON] et al. [68] presented a model whose performance, when tested on a complex set in which four errors were injected into a sentence, dropped drastically to 60.26%. [PERSON] et al.