# Sign Language Interpreting System Using Recursive Neural Networks
## 6 Conclusions  
In this paper, we proposed an MSL translator based on an RNN. We explored four main structures, two models constructed using LSTM networks, one model constructed utilizing Bidirectional LSTM, and one model with GRU. We used the spatial position of the body parts involved in signaling to predict words and build a custom dataset to evaluate the performance of the four models. The experiments we conducted show that Model 1 achieved higher performance among the models we proposed whereas performs with almost the same performance compared with similar approaches proposed previously by other authors in offline scenarios. On the other hand, the results for MSL translation in the online scenario exhibited higher performance as the number of AELVT errors is lower compared to the corresponding error level achieved by similar approaches (Table 7); our system reduces the errors in 67.47%. We observed that the bidirectional model failed to interpret words that look alike and share the same spatial positions. For example, the model confuses the words yes and eat. Also, the GRU-based model has a performance similar to that of model 1, but its drawback is that it triggers false positives at the early stages of signaling of the word, it needs to collect several coordinates to properly predict the corresponding word. The main limitation of the systems is that the translation of MSL signals is limited to the words in the dataset used for training, which limits its widespread application. In future work, it is proposed to incorporate training of more words into the network, especially those essential for basic communication (asking for help, directions, etc.) to verify if the system is scalable. Another important research direction is to reduce the errors that occur before the signaling of the word is complete. Another work is to translate also into an audio signal. This would make interactions between hearing and deaf people simpler and more natural. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Word** & **Bad Translations** & **Average Error** \\ \hline Hello & 1 & 0.05 \\ \hline Sorry & 2 & 0.1 \\ \hline You’re Welcome & 2 & 0.1 \\ \hline Yes & 1 & 0.05 \\ \hline No & 2 & 0.1 \\ \hline Thank You & 0 & 0 \\ \hline Good & 1 & 0.05 \\ \hline Bad & 1 & 0.05 \\ \hline Education & 4 & 0.2 \\ \hline Pencil & 0 & 0 \\ \hline House & 0 & 0 \\ \hline Table & 1 & 0.05 \\ \hline Today & 1 & 0.05 \\ \hline Mom & 1 & 0.05 \\ \hline Dad & 1 & 0.05 \\ \hline Please & 1 & 0.05 \\ \hline Help & 1 & 0.05 \\ \hline To eat & 0 & 0 \\ \hline To have & 12 & 0.6 \\ \hline To tolerate & 12 & 0.6 \\ \hline Average error in live video & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average error in live video translation. These results demonstrated the potential of the proposed models in scenarios such as teaching, learning, business transactions, and daily communications between deaf and non-deaf people. Conceptualization, E.A.B.-G. and N.M.-R.; methodology, E.A.B.-G.; software, N.M.-R.; validation, M.G.-L., [PERSON]-[PERSON] and [PERSON]-M.; formal analysis, H.P.-M.; investigation, M.G.-L.; resources, [PERSON]-[PERSON]; data curation, M.N.-M.; writing--original draft preparation, E.A.B.-G.; writing--review and editing, M.G.-L. and [PERSON]-[PERSON]; visualization, [PERSON]-M.; supervision, M.N.-M.; project administration, M.G.-L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The authors thank the Universidad Veracruzana for their support for this work, and [PERSON] want to thank the Conahcyt for their support. The authors declare no conflicts of interest.