# Recognition of Urdu Handwritten Characters Using Convolutional Neural Network
## 7 Conclusions  
In this paper, we made use of CNN (convolutional neural network) in recognizing and classifying Urdu handwritten characters. We also generated a novel dataset of Urdu handwritten characters and numerals. While performing experiments on our proposed dataset using CNN, we compared the results of different approaches in order to propose recommendations based on parameter tuning. The application of CNN in Urdu handwritten characters' classification provides a platform for developing applications for children at the beginner level to learn how to write Urdu characters and numerals correctly. Furthermore, there is a lack of standard data resource in the Urdu domain in order to generate benchmark results. In the field of machine learning, deep CNNs come with a revolutionary change by providing quite efficient results in comparison with conventional approaches. However, there are also some inherent questionable issues like there is a lack of knowledge of how to determine the number of levels and hidden neurons in each layer. Furthermore, a large-scale dataset is required to check the validity and efficiency of deep network models. Therefore, in our experiment, we had to train the CNN with many samples. In addition, finding a set of optimal parameters to generate error-free results is also a research issue. Moreover, our proposed classifier can be assessed using some other convolutional neural network models like two-dimensional BLSTM or bidirectional LSTM. Similarly, some complex future tasks like character recognition of rotated, mirror-text, and noisy images by extracting novel features could benefit. Moreover, we have also planned to develop a system that \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Grp** & **Grp** & **Grp** & **Grp** & **Grp** & **Grp** & **Grp** & **Grp** & **Grp** & **Grp** & **Classified** \\
**1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **as** \\ \hline
93\% & 0 & 0 & 1\% & 2\% & 0 & 0 & 2\% & 1\% & 1\% & 0 & 0 & Grp 1 \\ \hline
0 & 89\% & 0 & 3\% & 0 & 0 & 0 & 3\% & 2\% & 1\% & 1\% & 1\% & Grp 2 \\ \hline
0 & 2\% & 92\% & 0 & 2\% & 1\% & 0 & 0 & 0 & 0 & 2\% & 1\% & Grp 3 \\ \hline
2\% & 0 & 3\% & 91\% & 0 & 2\% & 0 & 2\% & 0 & 1\% & 0 & 0 & Grp 4 \\ \hline
2\% & 0 & 5\% & 0 & 93\% & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & Grp 5 \\ \hline
0 & 0 & 2\% & 3\% & 2\% & 90\% & 0 & 0 & 2\% & 0 & 1\% & 0 & Grp 6 \\ \hline
0 & 0 & 0 & 0 & 0 & 0 & 93\% & 4\% & 0 & 0 & 1\% & 2\% & Grp 7 \\ \hline
0 & 2\% & 3\% & 0 & 0 & 0 & 0 & 87\% & 4\% & 1\% & 1\% & 2\% & Grp 8 \\ \hline
3\% & 1\% & 0 & 0 & 0 & 0 & 0 & 0 & 89\% & 0 & 3\% & 4\% & Grp 9 \\ \hline
0 & 2\% & 0 & 0 & 2\% & 0 & 0 & 3\% & 1\% & 90\% & 0 & 2\% & Grp 10 \\ \hline
0 & 1\% & 0 & 0 & 1\% & 0 & 0 & 0 & 3\% & 0 & 91\% & 0 & Grp 11 \\ \hline
3\% & 2\% & 0 & 0 & 0 & 1\% & 1\% & 0 & 2\% & 0 & 1\% & 90\% & Grp 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Confusion matrix of _n_-fold cross-validation (10-fold) results for Urdu handwritten characters classification. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Grp** & **Grp** & **Grp** & **Grp** & **Grp** & **Grp** & **Grp** & **Grp** & **Grp** & **Grp** & **Classified** \\
**1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **as** \\ \hline
91\% & 3\% & 0 & 0  
should recognize individual Urdu characters rather than in groups. Since data science is continuously providing multifaceted large-scale datasets, it is essential to design and develop more efficient CNN models that are cost effective in the utilization of resources like memory, computational bandwidth, etc. According to Table 4, our proposed model was significantly better than the approaches used in the related literature in terms of the number of parameters and the amount of calculation.