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<p>Extracting key information from vast amounts of documents and data plays a crucial role in knowledge graph construction, intelligence analysis, decision support, and multimodal information retrieval (such as speech sentiment analysis and invoice error detection). While end-to-end OCR-free methods avoid the error propagation issues of traditional two-stage models, they often struggle to balance the extraction of fine-grained character details with the modeling of complex global layouts. To address this, this paper proposes a novel hybrid encoder architecture that synergizes the inductive bias of Convolutional Neural Networks (CNNs) with the global context modeling of Swin Transformers. Unlike standard symmetric architectures, we introduce a geometry-aware asymmetric downsampling strategy: a ConvNext (CN) module first compresses the height to retain horizontal resolution for character distinction, followed by a Swin-T module that reduces width to capture long-range row-column dependencies. Experimental results on the CORD and IIT-CDIP datasets demonstrate that the proposed method outperforms other OCR-free end-to-end information extraction methods in terms of information extraction accuracy and shows potential in advancing intelligent operations and maintenance.</p>

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Enhancing enterprise decision support via swin transformer-based OCR-free information extraction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月25日

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  • Changping Li,
  • Hao Tian

摘要

Extracting key information from vast amounts of documents and data plays a crucial role in knowledge graph construction, intelligence analysis, decision support, and multimodal information retrieval (such as speech sentiment analysis and invoice error detection). While end-to-end OCR-free methods avoid the error propagation issues of traditional two-stage models, they often struggle to balance the extraction of fine-grained character details with the modeling of complex global layouts. To address this, this paper proposes a novel hybrid encoder architecture that synergizes the inductive bias of Convolutional Neural Networks (CNNs) with the global context modeling of Swin Transformers. Unlike standard symmetric architectures, we introduce a geometry-aware asymmetric downsampling strategy: a ConvNext (CN) module first compresses the height to retain horizontal resolution for character distinction, followed by a Swin-T module that reduces width to capture long-range row-column dependencies. Experimental results on the CORD and IIT-CDIP datasets demonstrate that the proposed method outperforms other OCR-free end-to-end information extraction methods in terms of information extraction accuracy and shows potential in advancing intelligent operations and maintenance.