Vehicle tyre detection and text recognition using deep learning

Wajahat Kazmi, Ian Nabney, George Vogiatzis, Peter Codd, Alex Codd

Research output: Contribution to journalArticle (Academic Journal)peer-review

17 Citations (Scopus)
3780 Downloads (Pure)


This paper presents an industrial system to read text on tire sidewalls. Images of vehicle tires in motion are acquired using roadside cameras. Firstly, the tire circularity is detected using Circular Hough Transform (CHT) with dynamic radius detection. The tire is then unwarped into a rectangular patch and a cascade of convolutional neural network (CNN) classifiers is applied for text recognition. We introduce a novel proposal generator for localizing the tire code by combining Histogram of Oriented Gradients (HOG) with a CNN. The proposals are then filtered using a deep network. After the code is localized, character detection and recognition are carried out using two separate deep CNNs. The system presents impressive accuracy and efficiency proving its suitability for the intended industrial application.
Original languageEnglish
Number of pages12
Journal IEEE Transactions on Intelligent Transportation Systems
Early online date24 Jan 2020
Publication statusE-pub ahead of print - 24 Jan 2020
EventInternational Conference on Automation Science and Engineering - Vancouver, Canada
Duration: 22 Aug 201926 Aug 2019
Conference number: 15


  • Intelligent vehicles
  • deep learning
  • computer vision
  • tyre (tire) sidewall
  • Optical Character Recognition (OCR)


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