###### Abstract  
To enhance user experiences of reaching destinations in large, complex buildings, we have developed a _indoor navigation system using Unity and a smartphone_ called _INSUS_. It can reset the user location using a _quick response (QR)_ code to reduce the loss of direction of the user during navigation. However, this approach needs a number of _QR_ code sheets to be prepared in the field, causing extra loads at implementation. In this paper, we propose another reset method to reduce loads by recognizing information of naturally installed signs in the field using _object detection_ and _Optical Character Recognition (OCR)_ technologies. A lot of signs exist in a building, containing texts such as room numbers, room names, and floor numbers. In the proposal, the _Sign Image_ is taken with a smartphone, the sign is detected by _YOLOv8_, the text inside the sign is recognized by _PaddleOCR_, and it is compared with each record in the _Room Database_ using _Levenshtein distance_. For evaluations, we applied the proposal in 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 an averaged CER% lower than 10%. The combination of both _YOLOv8_ and _PaddleOCR_ decreases the execution time by 6.71s compared to the previous method. The results confirmed the effectiveness of the proposal. indoor navigation system; INSUS; location reset method; natural sign; text; YOLO; PaddleOCR +
Footnote †: journal: Article  
0000-0001-4400-0000-0000-00001-4400-0000-00001-4400-00000-00001-44000-00001-4400-00001-44000-00001-44000-00001-44000-00001-44000-0001-4400001-4400001-4400001-440001-44  
simultaneous localization and mapping (SLAM)_ algorithm to accurately detect and map the user's location. To reduce loss of direction during navigation, _INSUS_ employs the _user location reset_ method using a _quick response_ (\(QR\)) code. First, a user needs to scan a \(QR\) code that encodes the location information to initialize his/her initial position. Then, _SLAM_ calculates the user's location by measuring the distance from feature points captured by the smartphone's camera across frames. Finally, the gyroscope sensor is used to estimate the user's orientation to determine in which direction the user is facing. Unfortunately, _INSUS_ often faces the loss of direction problem during navigation. The loss of direction refers to localization errors during navigation due to small accumulated errors from the _SLAM_ localization components. Since the localization experiences errors, the generated path from the user's current position to the target position becomes inaccurate. This results in false or absent path guidance visualization. To mitigate it, a user needs to periodically scan the \(QR\) code to reset the current position. Therefore, a number of \(QR\) code sheets need to be placed around the building, which will add extra load to the implementation. In this paper, as an alternative _user location reset_ method, we propose the use of natural signs that have been allocated in buildings. These signs may indicate room numbers, room names, or floor numbers which can uniquely identify the location. The proposed method uses the _object detection_ technique to extract the sign from the captured image and the _Optical Character Recognition (OCR)_ technique to recognize the text from the sign [9; 10]. Specifically, _YOLOv8_ is adopted for _object detection_[11], and _PaddleOCR_ is adopted for _OCR_[12]. The recognized text is compared with the registered text in the database using the _Levenshtein distance_ to obtain the location information. Since the text recognized by _OCR_ may contain errors, including wrong or missing characters, the _Levenshtein distance_ between this text and each registered text in the database is calculated, and the one giving the shortest distance is selected as the matched one.