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

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.