Fast Word Alignment for Robust OCR in Vision Systems

Optical character recognition (OCR) has become a key technology in industrial and scientific applications, enabling machines to extract information from printed labels, product markings, and other text in images. While modern deep-learning-based OCR methods are highly accurate, they typically rely on a preceding text detection model to localize word regions. Running this detection model can be too time-consuming in scenarios with strict cycle time requirements. As a result, many users fall back on manually defining regions of interest or using rule-based image processing to locate text. These approaches, however, often produce suboptimal crops and lead to inaccurate recognition.

In this talk, Adler presents an alignment step introduced before recognition that automatically refines roughly defined text regions. By compensating for imprecise placement, this method improves reading accuracy even without a detection model. The result is faster, more flexible OCR workflows that maintain reliability with minimal computational overhead.