# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
###### Abstract  
Arabic handwritten-text recognition applies an OCR technique and then a text-correction technique to extract the text within an image correctly. Deep learning is a current paradigm utilized in OCR techniques. However, no study investigated or critically analyzed recent deep-learning techniques used for Arabic handwritten OCR and text correction during the period of 2020-2023. This analysis fills this noticeable gap in the literature, uncovering recent developments and their limitations for researchers, practitioners, and interested readers. The results reveal that CNN-LSTM-CTC is the most suitable architecture among Transformer and GANs for OCR because it is less complex and can hold long textual dependencies. For OCR text correction, applying DL models to generated errors in datasets improved accuracy in many works. In conclusion, Arabic OCR has the potential to further apply several text-embedding models to correct the resultant text from the OCR, and there is a significant gap in studies investigating this problem. In addition, there is a need for more high-quality and domain-specific OCR Arabic handwritten datasets. Moreover, we recommend the practical development of a space for future trends in Arabic OCR applications, derived from current limitations in Arabic OCR works and from applications in other languages; this will involve a plethora of possibilities that have not been effectively researched at the time of writing. deep learning; Arabic handwritten text recognition; optical character recognition; post-OCR correction; Arabic OCR; LSTM; CTC; Transformer; GANs; Arabic diacritization +
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274 million people [17]. Its script's morphological, lexical, and semantic richness make it one of the most challenging languages computationally [18]. Specifically, the Arabic writing system is from right to left and consists of 28 letters, each of which can take multiple forms, depending on its position in the word: alone, in the beginning, in the middle, or at the end. Moreover, some of the characters that can be turned into other characters simply by adding dots. Additionally, some characters have loops, while others have similar skeletons. Moreover, the Arabic writing system is connected, meaning that connections can be formed whenever two letters are combined. In addition, diacritics can be present in Arabic text in multiple forms, and different diacritics can change the meaning of a word slightly in most cases. The fonts are rich in their diversity as they originate from different eras and geographies. All these characteristics make recognition tasks difficult [19; 20; 21]. However, this challenge can also be viewed as an opportunity. Predominantly, the performance of OCR by an OCR system or engine involves the following steps: (1) image acquisition, in which the image is collected; (2) pre-processing, which involves steps to preprocess the previously acquired image, such as binarization and thinning; (3) segmentation, which can be performed either line-wise, word-wise, or character-wise; (4) feature extraction, in which the important visual features are extracted, which can help in the subsequent step; (5) classification, in which the recognition is performed by devising models that utilize the features extracted previously; and (6) post-processing techniques, which deal with the produced text to correct it further [22; 23]. Deep-learning techniques have achieved notable success in the fields of natural language processing and computer vision [24; 25; 26]. These techniques are centered around the use of different kinds of deep neural networks for training on a dataset, followed by the automatic extraction of the features, the performance of classification, and the production of the output of the classification. The training process is conducted by fine-tuning an architecture based on CNN-LSTM-CTC.