OCR Engines for License Plate Recognition: A Comparative Study of Tesseract, EasyOCR, PaddleOCR, and TrOCR - Afifah Khaerani Aziz; Marzuki Pilliang; Diana Kuniawati (Politeknik Negeri Batam, 2026)
Automatic License Plate Recognition (ALPR) is a critical component of Intelligent Transportation Systems (ITS); however, the Optical Character Recognition (OCR) stage remains a significant bottleneck when confronting diverse linguistic scripts, complex plate formats, and environmental degradations. This systematic literature review comparatively evaluates four primary OCR engines—Tesseract, EasyOCR, PaddleOCR, and TrOCR—to bridge the gap between constrained local optimizations and globally resilient applications. Adhering to the PRISMA 2020 guidelines, a comprehensive search across six major academic databases spanning January 2020 to May 2026 initially identified 647 records. Following rigorous screening processes, a final core corpus of 21 empirical studies was qualitatively synthesized to account for extreme cross-study hardware and dataset heterogeneity. The analysis reveals that no single engine is universally superior; efficacy is fundamentally dictated by their underlying neural architectures and contextual deployment parameters. Tesseract offers maximum computational efficiency but fails significantly on non-Latin and complex scripts due to legacy segmentation limits. EasyOCR provides an optimal accuracy-to-speed ratio, making it highly suitable for real-time edge device deployments. PaddleOCR excels in robust sequential decoding, delivering the highest exact-match rates required for high-stakes applications like automated tolling. Conversely, the Transformer-based TrOCR emerges as the definitive frontier for highly complex, irregularly spaced, and multilingual plates (e.g., Han, CIS region scripts), though its severe computational latency currently restricts it to cloud-based infrastructures. Furthermore, the synthesis establishes that dynamic, environment-aware preprocessing is a mandatory prerequisite to mitigate visual stressors such as motion blur and low illumination. This review provides a novel, context-driven deployment taxonomy, equipping researchers and developers with actionable guidelines to navigate the trade-offs between architectural accuracy, script diversity, and computational resource constraints. Finally, the review identifies unified Large Vision-Language Models (LVLMs) as the next-generation trajectory to resolve current multi-stage pipeline dependencies.