## 7 Conclusions and Future Research  
In this paper, we have presented an automatic classification method that classifies UML class diagrams. In our work, we used CNN for classifying UML class diagrams. Previous approaches ([PERSON] et al. [16], [PERSON] et al. [3]) have used Machine Learning techniques and mentioned the word \"accuracy\" only in their results. They have not mentioned whether it is the accuracy of the train set or test set. Thus, we are assuming they are getting the same accuracy on both datasets. We compared our results with other existing techniques and found that CNN results in better accuracy on training sets than other Machine Learning techniques for this problem. Accuracy for test sets is slightly less than some other approaches. The accuracy which we got on test sets was 85.41 and 86.63 for CNN without and with regularization, which is less as compared with other Machine Learning methods. This is because of a very small dataset. Generally, for Deep Neural Networks to result in good accuracy, the dataset must be large.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S.No.** & **Authors** & **Number of Images** & **Algorithm** & **Accuracy** \\ \hline
1. & [PERSON] et al. [16] (not a Machine & 200 & N/A & 89.0\% \\  & Learning approach) & 200 & N/A & 89.0\% \\ \hline
2. & [PERSON] et al. [3] & 1300 & SVM & 91.2\% \\ \hline
3. & [PERSON] et al. [3] & 1300 & Random Forest & 90.7–93.2\% \\ \hline
4. & [PERSON] et al. [29] & 19,506 & N/A & N/A \\  & (Not a Machine Learning approach) & 18,899 & image-to-model (i2m) & 94.4\% \\ \hline
5. & [PERSON] et al. [10] & 11,319 &
\begin{tabular}{c} Low-short learning with \\ Convolutional Neural Networks \\ \end{tabular} & More than 92\% \\ \hline
7. & Our research & 3282 & \
\begin{tabular}{c} CNN without regularization \\ \end{tabular} &
\begin{tabular}{c} Train: 94.51 \\ Test: 85.41 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy comparison with our paper.  
There is a need to generate a big dataset so that a larger variety of Deep Learning techniques can be applied to reduce the error rate and to get human-level accuracy. However, in the absence of a large dataset, a transfer learning approach can be considered which allows training deep architectures even if limited data are available [31]. Furthermore, the application of image processing tools, along with the Deep Learning algorithm will help to achieve optimal results. In another direction, there is scope to extend this model using Natural Language Processing (NLP) for the semantic understanding of diagrams. Text-recognition features will also play a vital role.  
Here, in this paper, we concentrated on classifying only one of the types of UML diagrams which is the UML class diagram from all other images (UML or non-UML diagrams). The scope can be extended to classify various UML diagrams such as structural UML diagrams (composite structure diagrams, deployment diagrams, package diagrams, profile diagrams, class diagrams, object diagrams, component diagrams), and behavioral UML diagrams (static machine diagrams, communication diagrams, use case diagrams, activity diagrams, sequence diagrams, timing diagrams, interaction overview diagrams).  
Conceptualization, M.G. and B.G.; data curation, B.G.; formal analysis, B.G., S.R.C. and J.S.; investigation, B.G.; methodology, M.G. and A.M.; supervision, M.G. and A.M.; validation, B.G., S.R.C. and J.S.; visualization, B.G., S.R.C. and J.S.; writing--original draft, B.G., S.R.C. and J.S.; writing--review and editing, M.G. and A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The dataset is available at the following link: [[https://zenodo.org/record/4252890#.XSJFjM0](https://zenodo.org/record/4252890#.XSJFjM0) zbIU]([https://zenodo.org/record/4252890#.XSJFjM0](https://zenodo.org/record/4252890#.XSJFjM0) zbIU).  
The authors declare no conflict of interest.