Search papers, labs, and topics across Lattice.
The paper introduces OCR-EDR, a rendering-aware framework designed to enhance closed-loop OCR systems by diagnosing and repairing errors in OCR predictions. By leveraging a fine-grained analysis of both the source image and its rendered output, OCR-EDR achieves high diagnostic accuracy and significantly improves the performance of OCR systems, particularly in handling complex formulas and structured text. The results demonstrate that the iterative repair process not only corrects errors but also boosts overall OCR performance metrics, including a notable increase in formula Case-F1 scores.
OCR-EDR transforms OCR error analysis into actionable repairs, achieving a remarkable 94.78% diagnostic accuracy and a 30.99-point boost in formula performance.
Although document OCR systems perform increasingly well on routine documents, complex formulas, structured text, and long-tail formats remain error-prone. OCR predictions may omit fine-grained content or hallucinate unsupported outputs, while equivalent encodings of the same visible content must be accommodated. Existing OCR evaluation methods mostly report aggregate metrics, offering limited support for analyzing case-level errors and improving OCR performance. We propose OCR-EDR (OCR Error Diagnosis and Repair), a rendering-aware framework that advances from fine-grained diagnosis to iterative repair. Given a source image, an editable OCR prediction, and its rendered image, OCR-EDR first jointly assesses whether the prediction and its rendering are consistent with the source, preserving valid predictions, including rendering-equivalent ones, while diagnosing and localizing genuine errors. It then applies executable edits and may request an updated rendering for iterative reassessment. We construct OCRErrBench from diverse real OCR predictions, covering text and formulas, exact and rendering-equivalent positives, and genuine errors, and develop the DocEDR model to execute the diagnosis--repair loop. On OCRErrBench, DocEDR achieves 94.78% diagnostic accuracy. It repairs 86.23% of erroneous inputs to visual consistency, raises formula Case-F1 by 30.99 percentage points over DOCR-Inspector-7B on DOCRcaseBench, and improves formula CDM by up to 4.62 percentage points on the identified Bad subsets of four OCR systems on UniMER-Test. These results show that OCR-EDR turns fine-grained OCR analysis into verified corrections and performance gains.
