# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 6 Analysis of Recent Works
### Post-OCR Correction Works [61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71]
The data were preprocessed using FastText with pre-training embedding and employing the BPE algorithm for infrequent words, which made a notable improvement of 32.38%. The model achieved a 70.91 F-1 score, 63.59 for recall, and 80.23 for precision on the QALB test set, a comparable performance to those of other SOTA models. [PERSON] [66] proposed a model that combines existing embedding techniques to detect and correct Arabic semantic errors that are not compatible with a sentence's contextual meaning. These techniques are SkipGram, FastText, and BERT. The model was tested on the ANC-KACST set, and the best model was averaged the FastText and BERT, achieving 88.9 for recall and 91.8 for F-1. [PERSON] [67] addressed the problem of ornamenting Arabic text with diacritics and the correction of spelling mistakes. They investigated encoder-decoder LSTM and Transformer models as problems in seq2 seq transcription. To train and test the models, ATB3 with 305K words and Tashkeela with 2312K words were used, with a split of approximately 15% for the testing on each. The results showed that the encoder using embedding and LSTM and the decoder using embedding and LSTM with Luong attention and Softmax activation at the dense output layer achieved the highest accuracy, of 99.78%. It was concluded that training with target-diacritized output corrects text with a high level of accuracy. [PERSON] et al. [68] noticed that introducing Arabic sentences into the Persian language complicated spelling-correction tasks. Thus, they utilized a conditional random field (CRF) RNN network and used 151,000 sentences to train the model. These sentences had artificial errors created by means of deletion, substitution, and insertion. The features were extracted from the dataset and used as inputs to the network to produce predictions. The results showed that the model achieved a good accuracy, of 83.36%, when a single error was introduced into the sentence. [PERSON] et al. [69] proposed an approach to diacritizing Arabic letters using four BiL-STMs. The diacritics were grouped into four categories: vowels, Sukoon, Shadda, and double-case endings (Tanween). The Tashkeela set was the source of data, from which the authors used ten files, each consisting of 10K lines, for the training, and one file for the testing. The data were separated into two parts: (1) input vectors that contained indexes of letters; and (2) output vectors that contained the diacritics' indexes according to their four groups. Subsequently, a concatenation operation occurred on both the input and the output vectors. Next, a sliding-window operation was performed to generate continuous and fixed-size data, with a size of 21 and a step of 7. Subsequently, these embeddings were fed to each corresponding BiLSTM layer during the training. Sigmoid activation was used for binary cases and Softmax for multiple cases. When including all the letters, the model resulted, at best, in a diacritization-error rate of 3 and 8.99% WER. [PERSON] et al. [70] created a model to diacritize Arabic poetry due to its rich compositions and the difficulty in diacritizing them. Transfer learning was leveraged to use features of pre-trained models. Sixteen classes of diacritics were formed, and four BiLSTM layers were used, followed by a Softmax activation. The dataset scarcity was combated with careful training on sub-sets of APCD2. The best model consisted of two parts: (1) a pre-trained classification model for meter prediction; and (2) four BiLSTM layers obtaining corresponding transfer-learning layers and the outputs of lower layers. The training was triphasic: first, the first stack layer was frozen and the second stack layer was trained on DS1; second, the whole model was retrained on DS1; and third, the whole model was retrained on DS2, which was smaller than DS1 but had a higher diacritics ratio. The results showed an improvement of 42% and a decrease in the diacritization-error rate from 6.08% to 3.54%. [PERSON] et al.