# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 3 OCR Process
### Recognition
The authors trained the ANN classifier on a dataset of printed Arabic text and then evaluated its performance on a separate test set. The results show that the system can accurately recognize the characters in the test set with high recognition rates. A KERAS model is used with a three-layer ANN classifier for character classification. Noise density and multi-spatial resolution matrices are used for evaluation, showing a fast, efficient, high-performance OCR system. [PERSON] and [PERSON] [93] review the various techniques used for text extraction from images and documents using OCR; they also discuss the challenges and limitations of text extraction. Reviewed papers are grouped on the basis of the types of OCR techniques that are used. The authors found that the most-used OCR techniques are based on machine learning, specifically deep learning. They also found that the OCR techniques that use multiple-recognition engines and preprocessing techniques perform better than single-recognition-engine-based techniques. Deep learning models such as CNN, RNN, and attention-based models are discussed in [94]. The paper discusses the performance of models on different Arabic handwritten datasets, and these models show much-improved character recognition rate, word recognition rate, and overall recognition rate. The researchers also discussed challenges dealing with different handwriting styles and sizes. The authors of [95] present a method for recognizing Arabic handwritten characters using different Holistic techniques, CNN, and deep learning models. At the same time, all of the techniques are well reviewed in this research, i.e., preprocessing, segmentation, feature extraction, recognition, and postprocessing. The authors found that using these models and techniques properly improves the recognition rate of the system by a significant margin. In [96], automatically extracting and processing text from images is discussed. The challenges that may arise in OCR stages are also explored, as well as the general phases of an OCR system, including preprocessing, segmentation, normalization, feature extrac  
Figure 7: Processes and techniques in each phase of the OCR system. tion, classification, and postprocessing. Additionally, the paper highlights OCR's main applications and provides a review of the state-of-the-art at the time of its publication. The approaches to processing text from documents are reviewed in [97]. In this review, text detection and text transcription are discussed. Text detection is a task whereby a process detects or finds text in a document or image. It is a difficult task but can be detected easily by box bounding and text detection as object detection. Text detection as object detection is challenging but is solved using computer vision tasks such as single-shot multi-box detectors and fast R-CNN models. Meanwhile, in text transcription, the text is extracted in editable form from the document or image of interest. Document layout analysis is the dominant part of selecting the region of interest. Then, the authors identify some datasets used in past research: ICDAR, Total-Text, CYW1500, SynthText, and the updated dataset FUNSD.