Hand-Punched Braille Document Recognition: A New Benchmark Dataset and a Robust Detection Transformer

Optical Braille recognition plays an important role in digitizing Braille documents and supporting accessible learning for blind and visually impaired learners. However, existing page-level Braille benchmarks provide limited coverage of smartphone-captured hand-punched documents with Braille dot appearance variation, which normally exhibit illumination variation and geometric distortion. To address this data gap, we introduce BrailleStuDoc, a benchmark dataset of student-hand-punched Braille documents captured with smartphones. It contains 331 images from distinct physical pages and 149,720 cell-level annotations covering all 63 non-empty six-dot patterns. To jointly address degraded local appearance cues and geometric variations in smartphone-captured hand-punched Braille documents, we propose BRIDGE, an illumination- and geometry-aware Braille cell detection Transformer. BRIDGE introduces Micro-Surface Feature Enhancement (MSFE) to strengthen local appearance representation under illumination and dot appearance variations, and Geometry-Aware Scale Modeling (GASM) to model the local geometric regularity of neighboring Braille cells under geometric distortion. Extensive experiments demonstrate the complementary value of BrailleStuDoc to existing public datasets and the effectiveness of BRIDGE, which achieves the best overall recognition performance among the compared methods.