New method aims to improve OCR transcription accuracy

2026-10-06

Researchers have developed GAD-RL, a novel approach to enhance the faithfulness of Optical Character Recognition (OCR) transcriptions generated by vision-language models. The method adaptively regulates teacher supervision during post-training to address the declining effectiveness of fixed guidance as student models improve.

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Researchers have developed GAD-RL, a new method to improve the accuracy of Optical Character Recognition (OCR) transcriptions from vision-language models. The method adaptively adjusts teacher supervision during post-training to maintain effectiveness as student models improve.

Key facts

GAD-RL is a novel approach designed to enhance the accuracy of OCR transcriptions from vision-language models.
The method adaptively regulates teacher supervision during post-training based on the student model's performance.
GAD-RL disables distillation for response groups with a task reward of at least 0.95.
On the CHAOS-Bench dataset, GAD-RL achieved 59.92% Micro Recall.
The research addresses the verifiable accuracy of text extracted from images by AI systems.

Source: arXiv · cs.AI

Reported by VERA Newswire.