# Recognition of Urdu Handwritten Characters Using Convolutional Neural Network
## 3 Literature Review
### Urdu Handwritten Character Recognition
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors** & **Features** & **Approach** & **UPTI Dataset** & **Accuracy (\%)** \\ \hline \multirow{3}{*}{[44]} & \multirow{3}{*}{pixels} & \multirow{3}{*}{BLSTM (Bidirectional Long Short Term Memory)} & 46\% Training 34\% Validation 20\% Test & \multirow{3}{*}{94.85\% 94.85\% 94.85\% 94.85\% 94.85\% 94.85\% 94.85\% 94.85\% 10\% Test} \\  & & & 46\% Training 4\% Validation 10\% Test & \multirow{3}{*}{94.97\% 16\% Test} \\ \hline \multirow{3}{*}{[42]} & \multirow{3}{*}{pixels} & \multirow{3}{*}{BLSTM (Bidirectional Long Short Term Memory)} & 46\% Training 4\% Validation 16\% Validation 16\% Test & \multirow{3}{*}{94.97\% 16\% Test} \\  & & & 68\% Training 16\% Validation 16\% Test & \multirow{3}{*}{98.25\% 16\% Test} \\ \cline{1-1} \cline{5-5}  & & & 16\% Validation 16\% Test & \multirow{3}{*}{98.25\% 16\% Test} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the proposed approach on the Urdu Printed Text Image (UPTI) dataset with other techniques [44]. Figure 6: Classification of the initial half forms on the basis of the number of strokes [23]. MDLSTM, multidimensional long short-term memory. In order to recognize the text, a one-dimensional long short term memory (BLSTM) based approach was proposed that was based on RNN (recurrent neural network), capable of restoring the previous sequence information. For evaluation purposes, the dataset was divided into 50% for training, 30% for validation, and 20% for testing and achieved a 6-8 percent error rate that can be improved using two-dimensional BLSTM, as proposed by the authors. Table 2 gives the summary of the accuracy reported on common datasets in the Urdu domain. In [27], the authors proposed a novel approach for Urdu text recognition at the character level, written in Nastaliq font by combining CNN (convolutional neural network) and MDLSTM. In the first phase, CNN was deployed to extract the characteristic features, which were then fed to MDLSTM in the second phase. This approach outperformed the state-of-the-art systems on the UPTI dataset. Table 3 shows the comparison of Urdu recognition on UPTI datasets. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **Article Reference** & **Accuracy** & **Approaches** \\ \hline UPTI [40] & [26] & 98\% & MDLSTM (Leven.Dist) \\  & [48] & 94.6\% & MDLSTM (CTC) \\ \hline UNHD & [46] & 92\% & BDLSTM \\ \hline CENPARMI[8] & [9] & 96.02\% & SVM \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy reported on common datasets. UNHD, Urdu Nastaliq Handwritten Dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Systems** & **Segmentation** & **Features** & **Classifier** & **Acc (\%)** \\ \hline
[40] & Holistic & Convolution & MDLSTM & 91.00\% \\ \hline
[42] & Implicit & Pixels & BLSTM & 88.94\% \\ \hline
[43] & Implicit & Pixels & BLSTM & 94.85\% \\ \hline
[49] & Implicit & Statistical & MDLSTM & 96.40\% \\ \hline
[50] & Implicit & Statistical & MDLSTM & 96.97\% \\ \hline
**Proposed**[27] & Implicit & Convolution & MDLSTM & 98.12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of Urdu recognition on UPTI datasets. Figure 7: Different samples of input images with noise and without noise [47].