OCR Training Needs to Cover Blank Pages
vanstriendaniel · x · 2026-07-16
The author reminds us that if you are building an OCR model, your training data should include some blank pages, or at least cover them in your evaluations.
The reason is that models are prone to errors when processing inputs with "no text." Including blank pages in data or evaluation helps identify these edge cases earlier, preventing failures in real-world scenarios after deployment.
More from Research
- Structural ensembles beat single predictions in TCR:pMHC generalization study — quaidmorris · 2026-07-22
- Structural ensembles, not single predictions, drive robust TCR:pMHC generalization — quaidmorris · 2026-07-22
- enFoldX turns AlphaFold3 ensemble noise into a TCR–peptide–MHC predictor — quaidmorris · 2026-07-22
- RSS launches under OMSF to push structural biology data modeling at scale — MoAlQuraishi · 2026-07-22
- enFoldX tops 8 neoantigen scans and an unseen-peptide benchmark — quaidmorris · 2026-07-22
- enFoldX reaches AUC 0.82 on human VDJdb and transfers to mouse at 0.76 — quaidmorris · 2026-07-22