## 5 Evaluations
### Performance Analysis of Text Extraction and Database Matching Function
#### 5.3.2 Accuracy of Text Extraction and Database Matching Function  
In this evaluation, the accuracy of the cleaned text was calculated by the _Character Error Recognition (CER)_ method [60, 61]. It represents the percentage of characters incorrectly recognized by the _OCR_ model compared to the total number of characters in the reference text. The lower values indicate better performance, while higher values indicate worse performance of the _OCR_ model.  
The effectiveness of the _user location reset_ method is determined by its ability to retrieve the room coordinates from the Room Database. Based on the experimental scenarios, we evaluated each room for each floor in both buildings. For each scenario, we took five images of each room on each floor in both buildings. Then, the _CER_ values were averaged and normalized to account for variations in the number of rooms. The results demonstrate the impact of incorporating the _YOLOv8_ model with _PaddleOCR_, as visualized in Table 6.  
Table 6 shows the results of a combination of the _YOLOv8_ model and _PaddleOCR_. It consistently achieved _CER_ lower than 10% compared to only the _PaddleOCR_. However, this approach still caused errors in text extraction. To address this problem, we employed the _Levenshtein distance_ algorithm in the _database matching function_. It compared the cleaned text with the list of room numbers in the Room Database and selected the corresponding  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**\#2 Building**} & \multicolumn{4}{c}{**\#3 Building**} \\ \hline  & Floor Level & 1F & 2F & 3F & 4F & 1F & 2F & 3F & 4F \\ \hline \multirow{3}{*}{Android} & _PaddleOCR_ & 42\% & 39\% & 36\% & 35\% & 36\% & 36\% & 39\% & 43\% \\ \cline{2-10}  & _YOLOv8_ model + & \multirow{2}{*}{2\%} & \multirow{2}{*}{5\%} & \multirow{2}{*}{8\%} & \multirow{2}{*}{3\%} & \multirow{2}{*}{5\%} & \multirow{2}{*}{3\%} & \multirow{2}{*}{2\%} \\  & _PaddleOCR_ & & & & & & & & \\ \hline \multirow{3}{*}{iOS} & _PaddleOCR_ & 51\% & 38\% & 32\% & 37\% & 34\% & 40\% & 34\% & 36\% \\ \cline{2-10}  & _YOLOv8_ model + & \multirow{2}{*}{2\%} & \multirow{2}{*}{2\%} & \multirow{2}{*}{3\%} & \multirow{2}{*}{4\%} & \multirow{2}{*}{4\%} & \multirow{2}{*}{3\%} & \multirow{2}{*}{3\%} & \multirow{2}{*}{2\%} \\  & _PaddleOCR_ & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: _CER_ results for every floor level on both buildings with and without the incorporation of the _YOLOv8_ model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Floor Level**} & \
\begin{tabular}{c} **Number of** \\ **Rooms** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ **Illuminance (LUX)** \\ \end{tabular} \\ \hline \multirow{4}{*}{\#2 Engineering} & 1 & 8 & 106.18 \\  & 2 & 8 & 116.91 \\  & Building & 3 & 8 & 91.67 \\  & 4 & 6 & 121.22 \\ \hline \multirow{4}{*}{\#3 Engineering} & 1 & 18 & 112.11 \\  & 2 & 16 & 123.68 \\  & Building & 3 & 17 & 115.62 \\  & 4 & 18 & 97.21 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specifications of evaluation environment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Samsung Galaxy S22 Ultra**} & \multicolumn{2}{c}{**iPhone X**} \\ \hline OS & Android & iOS \\ \hline GPU & Adreno730 & Apple GPU \\ \hline CPU & Octa-core (1 \(\times\) 3 GHz, 2 \(\times\) 2.5 GHz, 4 \(\times\) 1.8 GHz) & Hexa-core (2.39 GHz) \\ \hline Memory & 8 GB & 3 GB \\ \hline Camera & 108 MP & 12 MP \\ \hline \hline \end{tabular}
\end{table}
Table 5: Specifications of evaluation devices.  
room coordinates with the smallest _Levenshtein distance_. The sample results of the _database matching function_ are displayed in Table 7.