# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 3 OCR Process
### Segmentation  
Segmentation is an important step in OCR involving separation of an image into its constituent parts, such as lines, words, and characters, for recognition. Three types of segmentation techniques are mainly used, i.e., line, word, and character segmentation. Word segmentation, specifically, is challenging in cursive languages such as Arabic due to a lack of clear separation between characters. Traditional segmentation methods in Arabic OCR rely on rules and heuristics based on character features, but deep learning techniques such as convolutional and recurrent neural networks have shown promising results in automatic segmentation. Accurate segmentation is crucial for recognition, and improving Arabic OCR segmentation can have significant implications for document digitization, text-to-speech conversion, and language translation. The techniques used in [67] include image processing techniques such as thresholding to convert the image into a binary image, morphological operations to perform operations such as erosion and dilation on the binary image, and connected component analysis to identify and label connected regions in the image. The authors use two databases for evaluating the performance of the proposed method. The first database is the publicly available RDI-Arabic dataset, which consists of 1000 images of Arabic text documents. The second database is a new dataset created by the authors and consists of 1000 images of Arabic text documents. The research results show that the proposed image-processing techniques can accurately segment Arabic text documents into text lines, words, and characters with a high degree of accuracy. The researchers use several evaluation metrics, such as F-score, precision, and recall, to evaluate the performance of the proposed method. The results show that the proposed method outperforms traditional methods regarding segmentation accuracy. Urdu language characters are the super-set of Arabic language characters, and certain challenges are faced when performing segmentation of Urdu-like cursive languages [68]. Arabic is mainly written in Naskh, while Urdu is written in Nastaliq style. There are some challenges in the segmentation of Urdu script, i.e., cursive nature, difficult fonts such as Nastaliq, and letters changing their shape into different forms as required in the word; and cue points are hard to find in the Naskh or Nastaliq style. That is why segmentation is challenging, and character segmentation has been considered difficult in previous research. Researchers mostly used the projection profile method for segmentation, which can perform a vertical projection of the given text. However, in Arabic or Urdu, text writing starts from right to left and top to bottom, so vertical and horizontal projection is required. Analytical approaches are difficult and give the wrong character recognition results, but explicit and implicit recognition systems also give better accuracy. At the same time, holistic approaches are considered best by researchers for better accuracy with the correct recognition. Furthermore, there are better approaches than segmentation-free approaches for large vocabularies. For a reasonable accuracy rate, segmentation should perform well using the approaches that are correct and appropriate for segmentation. [PERSON] et al. [69] presented a survey to discuss the methods used by previous researchers. It discussed OCR systems, tools, applications, phases, and methods. There are two types of documents, i.e., unilingual and multilingual documents. Some OCR systems are discussed, i.e., Google Docs OCR, Tesseract, ABBY FineReader, Transym, and I2 OCR, which help to provide services and help to extract text from different types of documents with different languages, whether they are unilingual or multilingual. Multilingual documents contain text from multiple languages, and the techniques used are binarization, layout analysis, page segmentation, preprocessing, feature extraction, classification, and recognition.