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Multitasking vision language models for vehicle plate recognition with VehiclePaliGemma.

Nouar AlDahoul1, Myles Joshua Toledo Tan2, Raghava Reddy Tera3

  • 1Computer Science, New York University Abu Dhabi, Abu Dhabi, UAE.

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|July 18, 2025
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This study introduces VehiclePaliGemma, a fine-tuned visual language model (VLM) that significantly improves license plate recognition (LPR) accuracy for distorted images. It outperforms existing methods, achieving 87.6% accuracy in challenging conditions.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • License Plate Recognition (LPR) traditionally uses Optical Character Recognition (OCR) but struggles with image distortions like noise, blurring, and close characters.
  • Existing LPR methods require substantial improvements for accurate recognition, particularly with challenging image quality.

Purpose of the Study:

  • To evaluate the efficacy of various Visual Language Models (VLMs) in overcoming LPR challenges.
  • To introduce and validate "VehiclePaliGemma", a specialized VLM for robust license plate recognition.

Main Methods:

  • Evaluated multiple VLMs (GPT-4o, Gemini 1.5, PaliGemma, Llama 3.2, Claude 3.5 Sonnet, LLaVA, VILA, moondream2) for license plate recognition.
  • Developed and fine-tuned "VehiclePaliGemma" using a dataset of Malaysian license plates under complex conditions.
  • Compared VehiclePaliGemma against state-of-the-art methods and other VLMs.

Main Results:

  • VehiclePaliGemma achieved a superior accuracy of 87.6% on a challenging dataset.
  • The model demonstrated efficient processing at 7 frames per second on an A100-80GB GPU.
  • Explored VehiclePaliGemma's multitasking ability in identifying plates from multiple vehicles with varied orientations and conditions.

Conclusions:

  • VehiclePaliGemma significantly enhances license plate recognition accuracy, especially for distorted and complex images.
  • VLMs offer a promising advancement over traditional OCR-based LPR systems.
  • The fine-tuned VehiclePaliGemma model shows potential for real-world applications requiring high-accuracy LPR.