Dashcam Footage Analysis Tool for Timestamp and License Plate Evidence Extraction

Abstract

Dashcam footage has become an important source of digital evidence in traffic accident analysis and criminal investigations. However, manual examination of dashcam videos is time-consuming, prone to human error, and often lacks systematic verification of evidence integrity. Existing tools provide limited automation and insufficient forensic validation, which may compromise the reliability and admissibility of extracted evidence. This project presents the development of a Dashcam Footage Analysis Tool for Evidence Extraction that automates key forensic processes and enhances evidence reliability. The system focuses on video upload, time-stamp extraction, tamper detection, license plate detection, and automated forensic report generation. The tool is developed using the Prototyping Model to allow iterative refinement based on user feedback. It is implemented using Python and Flask, with OpenCV and Tesseract OCR for video frame analysis and timestamp extraction. At the same time, SHA-256 cryptographic hashing is employed to ensure evidence integrity through tamper detection. Experimental testing demonstrates that the system can accurately extract timestamps, identify video modifications through hash comparison, and generate structured forensic reports suitable for investigative purposes. The developed tool provides a practical and user-friendly solution for dashcam video forensics, improving investigation efficiency and increasing confidence in the authenticity of digital evidence.