## 6 Conclusions  
This paper proposed the _user location reset_ method using object recognition as an alternative approach in _INSUS_. The integration between _Unity_ (_INSUS_) and Python (_SEMAR_ server) is facilitated through HTTP communication using the REST API service. _YOLOv8_ is adopted to locate and isolate the _sign image_. _PaddleOCR_ is used to extract the text information from the _sign image_. The _database matching function_ with the _Levenshtein distance_ is applied to detect the same room number from the database. The location identified from the _database matching function_ is then used to update the user's current position within the building, completing the method's workflow.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Implementation**} & \multirow{2}{*}{**Cleaned text**} & _Levenshtein Distance_ & \multirow{2}{*}{**Room Number**} \\ \cline{3-4} \multicolumn{1}{c}{**Building**} & & & & _Levenshtein Distance_ & \\ \hline \multirow{3}{*}{\#2 Engineering} & 1F & d1d2 & 1 & d102 \\  & 2F & d205 & 0 & d205 \\  & 3F & d01 & 1 & d301 \\  & 4F & d4o1 & 1 & d401 \\ \hline \multirow{3}{*}{\#3 Engineering} & 1F & e1l3 & 1 & e113 \\  & 2F & e2l2 & 1 & e212 \\  & Building & 3F & 3303 & 1 & e303 \\  & 4F & e401 & 0 & e401 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Sample results of _Levenshtein distance_ from two implementation buildings.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**\#2 Engineering Building**} & \multicolumn{3}{c}{**\#3 Engineering Building**} \\ \hline Floor Level & 1F & 2F & 3F & 4F & 1F & 2F & 3F & 4F \\ \hline _PaddleOCR_ & 11.7 & 11.2 & 10.57 & 10.8 & 11.16 & 11.05 & 10.72 & 10.47 \\ \hline _YOLOv8_ model + _PaddleOCR_ & 4.22 & 4.17 & 3.93 & 4.34 & 3.49 & 4.46 & 4.81 & 4.56 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Execution time (seconds) in #2 and #3 Engineering Buildings.  
For evaluations, we applied the proposal to two buildings in Okayama University, Japan. The results show that _YOLOv8_ achieved [EMAIL_ADDRESS] 0.995 and [EMAIL_ADDRESS]:0.95 0.978, and _PaddleOCR_ could extract text in the _sign image_ accurately, with the averaged CER lower than 10%. The _database matching_ using _Levenshtein distance_ predicted the correct room number from the database, thereby accurately providing the coordinates to reset the user's location. The combination of both _YOLOv8_ and _PaddleOCR_ decreased the execution time by 6.71 s. Following the results of the proposed approach, the benefits include improved user location reset accuracy after incorporating the object detection function, reduced execution time, and the elimination of the need to install and maintain \(QR\) codes. This makes the system more flexible and easier to implement in various environments. The results confirmed the effectiveness of the proposal.  
For future works, we will increase types of _sign images_ to be recognized to improve the usability and accuracy of the proposal.  
Conceptualization, E.D.F., N.F. and S.S.; Methodology, E.D.F.; Software, E.D.F.; Validation, E.D.F.; Resources, S.S.; Data curation, A.L.H. and K.C.B.; writing--original draft preparation, E.D.F.; writing--review and editing, N.F. and Y.Y.F.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Ethical review and approval were not required for this study because the involvement of humans was limited to obtaining the indoor user location coordinates during the testing phase to validate the feasibility of our developed system.  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors thank the reviewers for their thorough reading and helpful comments and all the colleagues in the Distributed System Laboratory, Okayama University who were involved in this study.  
The authors declare no conflicts of interest.