Advancing Tire Sidewall Character Inspection with AI: TMSI Showcases New Recognition System at Rubber Industry Conference

Guangrao city, Shandong Province, China | May 16–18, 2026

From May 16 to 18, 2026, the 2026 Rubber Testing Committee Annual Meeting and the 8th Rubber Industry Innovation & Development Technical Seminar was held in Guangrao city, Shandong Province, China.

TMSI was invited to deliver a technical presentation “AI driven Tire Sidewall Character Recognition System Based on Line-Scan Photometric Stereo Imaging,” highlighted its latest progress in AI-driven tire sidewall character inspection.

Tire sidewall character recognition has long time challenges such as reflections on curved surfaces, low grey contrast, mixed fonts, character deformation, and complex symbol combinations. To address these industry pain points, TMSI introduced an integrated system solution combining large AI model-based character recognition and detection with high-quality imaging.

Key highlights of the system include:

l High-quality imaging for complex tire sidewall surfaces
The system uses line-scan photometric stereo imaging and multi-angle illumination to enhance the 3D features of tire sidewall characters, improving image stability under reflective, low-contrast, and shallow-marking conditions.

l AI-based character recognition and detection
Based on deep OCR and large AI model-based recognition, the system can adapt to mixed fonts, deformed characters, incomplete markings, and complex symbol combinations.

l Stable operation through precision mechatronic design
The system features slip-free synchronized rotation, adaptable tire positioning, and a rigid anti-vibration structure to support reliable inspection performance.

According to the presentation, the system can achieve recognition accuracy of up to 99.95%, with full-circumference inspection completed within 2 minutes per tire.

The presentation attracted strong interests from tire manufacturers and industry experts. TMSI will continue to focus on tire inspection, intelligent recognition, and digital quality management, helping the tire industry move toward with smarter and more reliable manufacturing.