# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 3 OCR Process
### Evaluation  
Evaluation measures the accuracy and quality of the recognized text output. OCR evaluation typically involves comparing the recognized text to the original input image or document and calculating various performance metrics to assess the quality of the OCR output. Some common metrics used in OCR evaluation include character error rate (CER), word error rate (WER), recall, precision, and F1 score. OCR evaluation can be performed using various methods depending on the goal of the application, such as manual inspection, crowdsourcing, or automated evaluation software. Evaluation is an important step in OCR development and deployment, as it allows developers and users to assess the accuracy and quality of the OCR output and make improvements or adjustments to the OCR system as needed. An Arabic OCR evaluation tool is discussed in [103]. The Arabic language is difficult to recognize due to its characters/alphabets' behavior in different words. Arabic OCR's accuracy could be higher because of improper evaluation of performance metrics. Different tools and software have been introduced to help find the performance and accuracy of the applied algorithms. The introduced software is built specially to check Arabic OCR; it checks the performance based on objectives, i.e., accuracy and evaluation metrics. These tools briefly describe the text in characters with or without dots, baseline, and diacritics and the class in which the text lies. These tools include Tesseract, easy OCR, Paddle-Paddle OCR, and PyMuPdf. Recognition rate (RR), character error rate (CER), and word error rate (WER) are the evaluation measures used by these programs to evaluate OCR output. [PERSON] et al. [104] discuss various open-source tools for Arabic OCR systems containing Tesseract ([[https://github.com/tesseract-ocr/tesseract](https://github.com/tesseract-ocr/tesseract)]([https://github.com/tesseract-ocr/tesseract](https://github.com/tesseract-ocr/tesseract)), accessed on 24 February 2023), OCRad ([[https://www.gnu.org/software/ocrad/](https://www.gnu.org/software/ocrad/)]([https://www.gnu.org/software/ocrad/](https://www.gnu.org/software/ocrad/)), accessed on 24 February 2023), and GOCR ([[https://jocr.sourceforge.net/](https://jocr.sourceforge.net/)]([https://jocr.sourceforge.net/](https://jocr.sourceforge.net/)), accessed on 24 February 2023). These tools are evaluated using an Arabic dataset, and Tesseract gives better recognition results. However, it is slower than other evaluated tools. These tools give better accuracy for high-resolution documents, and the accuracy gets worse as the quality of documents decreases. The authors of [105] provide an overview of existing tools and metrics used to evaluate the OCR system in previous research. Their paper covers traditional evaluation techniques and discusses their pros and cons. It also discusses evaluation metrics such as character error rate, word error rate, and recognition rate. The detailed review also discusses the many challenges involved in evaluating the performance of the OCR system. The performances of generative and discriminative recognition models for offline Arabic handwritten recognition are compared using generatively trained hidden Markov modeling (HMM), discriminatively trained conditional random fields (CRF), and discriminatively trained hidden-state CRF (HCRF) in [106]. The study presents recognition outcomes for words and letters and assesses the efficiency of all three strategies using the Arabic IFN/ENIT dataset. [PERSON] et al. [107] discussed offline handwritten word recognition in Devanagari. A holistic-based approach is used in this approach, wherein a word is considered a single entity, and the approach processes it further for extraction and recognition. A class of 50 words recognizes every word based on a feature vector set, uniform zoning, diagonal, centroid, and feature-based.