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Paper WE-LA-T4.2

Sural, Shounak (Carnegie Mellon University), Rajkumar, Ragunathan (Carnegie Mellon University)

TrafficSignReader: Real-Time Zero-Shot Recognition of Text-Based Traffic Signs

Scheduled for presentation during the Regular Session "S04c-Intelligent Perception and Detection Technologies for Connected Mobility" (WE-LA-T4), Wednesday, November 19, 2025, 16:20−16:40, Surfers Paradise 1

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 19, 2025

Keywords Real-time Object Detection and Tracking for Dynamic Traffic Environments, Deep Learning for Scene Understanding and Semantic Segmentation in Autonomous Vehicles

Abstract

Locating and reading traffic signs is an integral aspect of driving in general and is considered safety-critical for autonomous vehicles (AVs). However, the sheer number of disparate traffic signs and a lack of public datasets pose unique challenges for designing computer vision algorithms to recognize a wide variety of signs. In this paper, we propose TrafficSignReader, a three-stage framework for recognizing text-based traffic signs without the need for category-specific signdata. TrafficSignReader comprises an object detection stage,an optical character recognition step, and a language-based matching stage for individual sign recognition. Our approach is robust against motion blur, occlusion, glare from streetlights as well as weather conditions. We also create the Textual Traffic Sign Dataset, consisting of about 10,000 textual traffic sign images across 190 classes, covering most of the textual traffic sign categories found in the United States. TrafficSignReader recognizes textual traffic signs across all these classes with anF1-Score of 95.1%, comparing very favorably with respect to the current state of the art in terms of accuracy, breadth of coverage, generalizability and run-time efficiency. It runs at 10 fps using a lightweight GPU on our CMU AV, reading traffic signs over 50m away and making it feasible for real-world deployment. Our code and dataset are available at https://github.com/ssuralcmu/TrafficSignReader.git.

 

 

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