Challenges in Automatic License Plate Recognition System Review

Mustafa, Twana; Karabatak, Murat

doi:10.1109/isdfs58141.2023.10131688

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…However, the accuracy of all methods decrease in the videos recorded at night. This reduction in accuracy is due to the lighting conditions, which agrees with [51]. Tesseract still achieves high accuracy during day-and nighttime, while the accuracy deterioration of GOCR and OCRAD is greater than that of Tesseract.…”

Section: Text Extraction Resultssupporting

confidence: 80%

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Digital Forensic Analysis of Vehicular Video Sensors: Dashcams as a Case

Daraghmi,

Shawahna

2023

Sensors

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Dashcams are considered video sensors, and the number of dashcams installed in vehicles is increasing. Native dashcam video players can be used to view evidence during investigations, but these players are not accepted in court and cannot be used to extract metadata. Digital forensic tools, such as FTK, Autopsy and Encase, are specifically designed for functions and scripts and do not perform well in extracting metadata. Therefore, this paper proposes a dashcam forensics framework for extracting evidential text including time, date, speed, GPS coordinates and speed units using accurate optical character recognition methods. The framework also transcribes evidential speech related to lane departure and collision warning for enabling automatic analysis. The proposed framework associates the spatial and temporal evidential data with a map, enabling investigators to review the evidence along the vehicle’s trip. The framework was evaluated using real-life videos, and different optical character recognition (OCR) methods and speech-to-text conversion methods were tested. This paper identifies that Tesseract is the most accurate OCR method that can be used to extract text from dashcam videos. Also, the Google speech-to-text API is the most accurate, while Mozilla’s DeepSpeech is more acceptable because it works offline. The framework was compared with other digital forensic tools, such as Belkasoft, and the framework was found to be more effective as it allows automatic analysis of dashcam evidence and generates digital forensic reports associated with a map displaying the evidence along the trip.

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Section: Text Extraction Resultssupporting

confidence: 80%

“…Ref. [51] presented the challenges in license plate recognition using OCR. Internal challenges are related to the software used for processing and the camera hardware and include resolution, view angles, focus speed, the processor and internal memory.…”

Section: Dashcam Text and Speech Analysismentioning

confidence: 99%