## 5 Results of Reference Model  
Tesseract model [18; 40] is a model based on recurrent neural networks that use four layers of 64, 96, 96, and 192 LSTM blocks. The first, second, and fourth layers are forward, the third layer is backward, and the number of nodes in the output layer is 90. Tesseract achieved the best results in Persian OCR and because of that, it was considered as a baseline model in this work. Both test data categories are fed to the Tesseract and its performance is compared with Bina. As it is mentioned earlier, Tesseract cannot recognize English words in the Persian context and inserts Persian number instead. As a result, its accuracy on the Persian-English test set is 1.33% since the number of wrongly added words to text is high. So, the correctness evaluation is used instead of accuracy since it does not consider the added words. The results show an accuracy of about 91.61% on the Persian-only test set and the correctness of 71.65% on the Persian-English test set. More information can be found in Table 10.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Font**} & **Accuracy** & **Accuracy** & **Correctness** \\  & **Persian Only** & **Persian \& English** & **Persian \& English** \\ \hline Arial & 94.73 & 2.00 & 69.40 \\ Calibri & 92.70 & 2.33 & 68.17 \\ Homa & 84.22 & 1.00 & 69.83 \\ Lotus & 91.55 & 1.00 & 76.35 \\ Mitra & 92.59 & 1.33 & 68.75 \\ Nazanin & 93.67 & 1.00 & 76.97 \\ Tahoma & 92.04 & 1.67 & 66.44 \\ Times New Roman & 94.39 & 1.33 & 69.17 \\ Traffic & 87.86 & 1.00 & 72.87 \\ Yagut & 94.25 & 1.00 & 75.59 \\ Zar & 91.68 & 1.00 & 74.64 \\ \hline Average & 91.61 & 1.33 & 71.65 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of reference model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**1 st Convolution** & **Number of Filters in** & **Pooling** & **Output** & **Average of Accuracy** \\
**Layer** & **Convolution Layer** & **Layer** & & **of All Fonts and Sizes** \\ \hline
3,3 & 16 & 3,3 & 16 & 87.93 \\
3,3 & 32 & 3,3 & 32 & 87.59 \\
5,5 & 16 & 5,5 & 16 & 84.60 \\
5,5 & 32 & 5,5 & 32 & 85.15 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Different CNN setting with one layer for feature extraction with the best model deep BLSTM setting.  
To thoroughly evaluate the proposed model with Tesseract, we trained the Tesseract model with our collected dataset. Since the collected dataset includes 165 unique characters, the number of neurons in the output layer is increased from 90 to 165. Table 11 shows its accuracy.  
Figure 10 draws an analogy between Bina and Tesseract in terms of accuracy on the Persian-only test set. As it was expected, Bina outperforms Tesseract due to taking advantage of using BLSTM instead of LSTM. Figure 11 demonstrates the correctness of the two models in the Persian and English test set. Also, Figure 12 illustrates the accuracy of the trained Tesseract model and the proposed model.  
Figure 11: Average of the correctness of reference model and the proposed model on Persian and English test set.  
Figure 10: Average of the accuracy of the reference model and the proposed model on Persian only test set.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Font** & **12** & **14** & **18** & **Average of Fonts** \\ \hline Arial & 86.89 & 95.32 & 61.97 & 81.39 \\ Calibri & 81.08 & 94.53 & 62.52 & 79.38 \\ Homa & 54.77 & 54.25 & 24.38 & 44.47 \\ Lotus & 83.20 & 81.18 & 68.72 & 77.70 \\ Mitra & 72.24 & 71.82 & 32.27 & 58.78 \\ Nazanin & 80.81 & 75.03 & 71.80 & 75.88 \\ Tahoma & 83.89 & 94.59 & 63.64 & 80.71 \\ Times New Roman & 83.71 & 95.20 & 62.92 & 80.61 \\ Traffic & 75.40 & 73.38 & 68.61 & 72.46 \\ Yagut & 87.56 & 82.89 & 76.84 & 82.43 \\ Zar & 79.27 & 75.32 & 71.32 & 75.30 \\ \hline Average of Accuracy & 78.98 & 81.23 & 60.45 & 73.56 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Trained Tesseract accuracy on Persian and English test set with four layers and 64, 96, 96 and 192 LSTM blocks.