Scene Text Recognition
Overview
Scene text recognition studies how to locate and transcribe writing in photographs and video, where text may be curved, rotated, blurred, or mixed with clutter. Systems commonly combine text detection with CNN or Transformer recognizers, using either CTC or autoregressive decoding; newer work also uses vision-language models, synthetic data, self-supervision, and script-aware expert routing. Benchmarks increasingly test multilingual and language-specific reading, artistic text, long inputs, video, and degraded or misleading scenes, not just standard English word images. A central challenge is balancing visual character evidence with linguistic context: context can help with ambiguity but can also cause models to replace rare or clearly visible text with more plausible words, while low-resource scripts and difficult real-world conditions remain weaknesses.
Latest papers
29 papers2 papers in the last four weeks, down 60% on the four weeks before. 0.0% of all new papers.