Search papers, labs, and topics across Lattice.
This paper introduces NaviDC-OCR, a unified framework designed to enhance document parsing by addressing the limitations of existing Vision-Language Models (VLMs) in handling geometric distortions and structural reasoning. By integrating deformation-aware learning and an adaptive sampling mechanism, NaviDC-OCR effectively improves the representation of complex layouts, while a content-structure decoupled learning strategy allows for better modeling of formula grammars and table structures. Extensive evaluations show that NaviDC-OCR achieves state-of-the-art performance across multiple benchmarks, including top rankings in the ICDAR 2026 Sci-ImageMiner Challenge.
Achieving state-of-the-art performance in document parsing, NaviDC-OCR tackles geometric distortions and structural reasoning challenges that plague existing models.
Document parsing aims to transform unstructured documents into structured and machine-readable representations. Recent advances in Vision-Language Models (VLMs) have significantly advanced document parsing. However, existing approaches still face two major challenges. First, decoupled VLM-based methods heavily rely on accurate layout analysis, where geometric distortions in camera-captured documents can introduce cascading errors. Second, although end-to-end VLM-based methods alleviate the dependence on explicit layout detection, they often suffer from redundant generation, hallucinations, and insufficient structural reasoning in high-resolution scenarios. To address these challenges, we propose NaviDC-OCR, a unified framework for document parsing. NaviDC-OCR introduces deformation-aware learning to incorporate geometric perception into VLMs and proposes an adaptive sampling mechanism for complex layout representation. Furthermore, a content-structure decoupled learning strategy is developed to explicitly model formula grammars and table structures, enabling more effective structured representation learning. Extensive experiments demonstrate that NaviDC-OCR achieves state-of-the-art performance across diverse document parsing benchmarks. It obtains overall scores of 96.87, 88.53 and 78.41 on OmniDocBench v1.6, Wild-OmniDocBench, and PureDocBench, respectively, and ranks first in the ICDAR 2026 Sci-ImageMiner Challenge. These results validate the effectiveness and generalization capability of NaviDC-OCR in complex document parsing scenarios.
