# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 2 Datasets
### Printed Arabic  
In Arabic OCR, printed, handwritten, and historical documents are used. To process printed Arabic documents, [43] presents top-down, bottom-up, and hybrid approaches and discusses the phases of preprocessing, segmentation, feature extraction, and classification.  
An efficient, font-independent word and character segmentation algorithm for printed Arabic documents is proposed in [44]. Profile projection is used for the font-independent technique. Interquartile Range (IQR) is used for word segmentation. For character segmentation, two approaches are used, i.e., the holistic approach (segmentation-free approach) and the analytical approach, which is a segmentation-based approach, and the process followed for this purpose is shown in Figure 3. For this purpose, ATPI is the dataset used to make a font-independent OCR system that achieves 97.51% accuracy.  
In [45], the authors propose a thinning algorithm in the preprocessing stage. A new chain code representation technique is proposed using an agent-based model for feature extraction from non-dotted Arabic text images. A character segmentation technique based on the extracted features is also introduced. A compression-based method is applied to recognize Arabic text in the classification stage. The system was tested on a public dataset and produced an accuracy of 77.3%.  
The authors of [46] demonstrate the effective use of unsupervised algorithms for writer attribution of historical scanned documents and forensic document analysis. Some distinct handwriting styles differ in various ways, including character size, stroke width, loops, ductus, slant angles, and cursive ligatures. Additionally covered are prior efforts on labeled data that provide excellent accuracy rates utilizing the Hidden Markov Model (HMM), Support Vector Machine (SVM), and semi-supervised Recurrent Neural Networks (RNN).  
Transformer-based models are a type of deep learning method to deal with sequential data [47]. Several metrics are used to evaluate the performance of the proposed method; those metrics are character error rate (CER) and word error rate (WER). Furthermore, results show that the proposed method improves the recognition rate of historical documents.  
The data generated by IoT devices such as the Quran Read Pen, which helps to read Quran specifically to illiterate or blind people [48], is shared via the Quranic Text Image Dataset (QTID). It contains 309,720 images of words and 2,494,428 characters taken from the Quran, which uses the sequence-to-sequence model and CNN and achieves a high recognition rate. The character recognition rate (CRR) with and without diacritics is about 97.60% and 97.05%, respectively, and the overall recognition rate of this model is 99.48%, while the CNN model gives the CRR with and without diacritics of about 98.90% and 98.51%, respectively.  
Feature extraction and classification techniques are used for character segmentation in ancient manuscripts for their preservation and information extraction [49].  
Figure 3: Character segmentation stages in order to recognize characters with maximum accuracy.