# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 3 OCR Process
### Recognition  
Recognition, also called classification in some previous works, is the process of identifying and assigning a specific character or set of characters to a given input image. After preprocessing and segmentation, the OCR system compares the extracted features of each character or group of characters with a set of predefined templates or models. The OCR system creates these templates during a training phase, where a large dataset of sample images is used to teach the system how to recognize each character. The classification/recognition process can involve several algorithms, including template matching, neural networks, and support vector machines. Template matching involves comparing the features of each character to predefined templates and selecting the template with the closest match to the recognized character. Neural networks and support vector machines use machine learning algorithms to learn and classify patterns in the data and can often achieve higher accuracy than template matching. A survey of feature extraction and classification techniques is presented in [82]. The techniques include digitization, preprocessing, segmentation, feature extraction, and post-processing. Statistical, structural, template matching, artificial neural network, and kernel classification methods are also discussed. An efficient feature-descriptor selection for improved Arabic handwritten word recognition is presented in [83]. The approach uses three image features, Histogram Oriented Gradient (HOG), Gabor Filter (GF), and Local Binary Pattern (LBP), for feature extraction, and trains a \(k\)NN algorithm to build models. The best model achieved an accuracy of 99.88%. A publicly available IFN/ENIT Arabic dataset is also introduced. The researcher use the global approach in the research, which is considered successful and in many cases is used more than the analytical approach. Various methods and techniques are used in multilingual OCR [84], including preprocessing, binarization, segmentation, feature extraction, and recognition. Segmentation uses three different approaches, i.e., top-down, bottom-up, and hybrid approach, including page, line, and word/character level. The research also explores the challenges to and limitations of current multilingual OCR systems, such as dealing with different scripts and languages and the need for large amounts of annotated training data. The paper highlights the importance of multilingual OCR for applications such as digital libraries, document archiving, and machine translation. Overall, the paper provides a comprehensive overview of the current state of multilingual OCR research and its potential applications. The paper highlights the importance of multilingual OCR for various applications such as digital libraries, document archiving, and machine translation.The authors of [85] discuss the ongoing design of a tool for automatically extracting knowledge and cataloging documents in Arabic, Persian, and Azerbaijan using OCR, text processing, and information-extraction techniques. A method using a combination of CNN and RNN for recognizing Arabic text in natural scene images is presented in [86]. The method utilizes an attention mechanism to focus on the most relevant parts of the image and improve text recognition accuracy. Two datasets are used in this research for training purposes, i.e., ACTIV and ALIF. ACTIV contains 21520 images of text lines, while ALIF contains 6532 images of text lines; both datasets are extracted from different news channels. Testing is done on the Arabic natural scene text dataset (ANST) the authors created. The proposed method is evaluated on this dataset, which shows that it outperformed the state-of-the-art methods for Arabic text recognition in natural scene images with an accuracy of 92.4%. The model uses CNN to extract features from the image and RNN to process the features and generate the text recognition output.