# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 3 OCR Process
### Postprocessing  
Postprocessing is the final step in the OCR process; it involves improving the accuracy and quality of the recognized text. Postprocessing techniques can include various methods, such as spell checking, contextual analysis, confidence scoring, and language-model integration. Spell checking involves comparing the recognized text against a dictionary of words to identify and correct spelling errors. Contextual analysis analyses the recognized text within the context of the surrounding text to identify and correct errors that may be caused by confusion with other words. Confidence scoring assigns a confidence score to each recognized character based on the certainty of the OCR system's recognition, and characters with lower confidence scores are flagged for review or correction. The language model is also used to analyze the recognized text in the language context. Postprocessing significantly improves the accuracy and quality of the recognized text. An overview of the different postprocessing techniques developed and applied to OCR output, including methods for correction of errors, text enhancement, and text restoration, is discussed in [98]. Various methods are used in postprocessing, including spell checking, grammar checking, lexicon-based correction, machine learning, and deep-learning-based approaches. The paper also discusses using different types of features, such as character-level, word-level, and document-level features, as well as preprocessing techniques, such as segmentation and normalization. Several approaches have been used in the postprocessing of OCR output to improve the accuracy and completeness of the recognized text:  
* Spell checking: checks the spelling of the recognized text and corrects any errors by comparing it to a dictionary [99]. * Grammar checking: checks the grammar of the recognized text and corrects any errors by comparing it to a set of grammar rules. * Lexicon-based correction: uses a lexicon (a list of words and their possible variations) to correct errors in the recognized text by comparing it to the lexicon and suggesting alternative words where there are errors. * Machine-learning-based approaches: uses machine learning algorithms, such as decision trees, random forests, and support vector machines, to correct errors in the recognized text. * Deep-learning-based approaches: uses deep learning algorithms, such as CNNs and RNNs, to correct errors in the recognized text. * Text enhancement: includes techniques to improve the recognized text's visibility, legibility, and readability, such as binarization, deskewing and smoothing of text. * Text restoration: includes techniques to recover missing or degraded text, such as text in-painting, completion, and restoration. The authors of [98] also present the results of various experiments and evaluations conducted to assess the performance of postprocessing techniques. These include comparisons of different methods and systems, evaluations of the effects of different types of features and preprocessing techniques, and evaluations of the performance of systems on different types of OCR output and languages. [PERSON] et al. [100] present a word-context and rule-based technique for OCR postprocessing. OCR is used to obtain text from scanned documents, and the output text is not always 100% accurate. Thus, after obtaining the text, postprocessing step is required to recognize and minimize the errors. The presented research is about printed documents, it lies in an offline OCR system. Cursive nature also causes problems in this step because characters in Arabic are connected, and there are other additional things such as diacritics and different shapes of each character in different words. These things bring more complications to this step. A very small amount of work has been done on the Arabic postprocessing technique because it shows a very high character and word error rate after recognition. Therefore, it is a less attractive side for researchers.