## 3 Related Work
### OCR Studies for Persian-Arabic Based Scripts  
[PERSON] and [PERSON] [16] worked on OCR for Kurdish (Sorani) texts in Persian-Arabic script. They discussed the features of the script in terms of its special diacritics and cursive style, and accordingly, they proposed a new set of rules for the segmentation process. They used optimized horizontal histogram projection for line segmentation and contour-based segmentation for character segmentation. The research used the Gamera [17] toolkit for classification and reported a 90.82% of accuracy for their approach. While conducting this study, we also noticed work on Sorani (Central Kurdish) OCR using Tesseract OCR engine (more detail could be found on its GitHub page at [[https://github.com/Shreeshrii/tesstrain-ckb](https://github.com/Shreeshrii/tesstrain-ckb)]([https://github.com/Shreeshrii/tesstrain-ckb](https://github.com/Shreeshrii/tesstrain-ckb)) [accessed on 7 August 2021]). The mentioned work created a language model for Sorani using synthetic data generated over 26 fonts (see Table 7). However, at the time of writing this paper, we could not find any papers, whether peer-reviewed or as work in progress, to explain more than what the GitHub page describes. [PERSON] et al. [18] used Artificial Neural Network (ANN) and Hidden Markov Model (HMM) models in the segmentation process of their Arabic OCR. Their focus was on both isolated and concatenated forms of Arabic characters. They reported a 100% recognition accuracy with ANN and 69% with HMM. For HMM, they reported an accuracy rate of 71% and 50% for isolated and concatenated letters, respectively. According to their results, the ANN method misidentified five letters, while HMM approach misidentified nine. However, it is not clear how they achieved a 100% accuracy with ANN while the method wrongly identified five letters. [PERSON] et al. [19] conducted a study for isolated Arabic letters using ANN with Backpropagation and Learning Vector Quantization (LVQ) method. The research showed a 98.81% accuracy. However, it reported an issue about the confusion between isolated and concatenated letter recognition. The Urdu language is also written from right to left except for the numerals that follow the similar languages wherein they are written from left to right [20]. Urdu consists of 38 letters and ten numeric characters. A study by [PERSON] et al. [21] proposed preprocessing techniques for OCR systems using four steps. The first step includes document scanning, orientation detection, skew correction, noise removal, binarization, and then other standard preprocessing methods. The second step is segmentation which separates the paragraphs and text lines. The third step is to extract features such as the right, left, top, and bottom points, curves, loops, crosses, and the height of characters in the text. The last step is the classification that uses fuzzy logic rules in two phases to recognize the diacritics and visible features. They used NastaTiq font of the size of 36 for the proposed method. They reported an accuracy rate \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{\hat{\pi}}\) & **Font Name** & \(\mathbf{\hat{\pi}}\) & **Font Name** & \(\mathbf{\hat{\pi}}\) & **Font Name** \\ \hline
1 & [PERSON] & 10 & Arial & 19 & Segoe UI Bold \\
2 & [PERSON] & 11 & Arial Bold & 20 & Speda \\
3 & [PERSON] OLD & 12 & NRT Bold, & 21 & Speda Bold \\
4 & [PERSON] & 13 & NRT Reg & 22 & Tahoma \\
5 & [PERSON] Bold & 14 & Peshang Des 1, & 23 & Tahoma Bold \\
6 & [PERSON] & 15 & RudawRegular & 24 & Ubuntu Kurdish 0.81 met \\
7 & Adobe Arabic Bold & 16 & Sarchia\_Banoka\_1 & 25 & Unikurd Chimen \\
8 & Arabic Typesetting & 17 & Segoe UI & 26 & Unikurd Goran \\
9 & Unikurd Web & 18 & Shasenem-kurd & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Fonts used by [PERSON] to fine-tune the Arabic language model for Sorani. of 100% and 94% for the baseline/single character identification and ligature/concatenated identification, respectively.