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Overview of EvaHan2026: The First International Evaluation of Ancient Chinese OCR and Layout Analysis

Ancient Chinese documents are vital for historical research, necessitating high-precision character recognition and layout analysis for digitization. This paper introduces EvaHan2026, the inaugural international shared task for simultaneous optical character recognition and layout parsing of ancient texts. The evaluation framework comprehensively assesses model performance across diverse calligraphic styles and complex structures, including main body text, interlinear annotations, and illustrations. Among thirteen participating teams, four successfully completed all tasks within the closed track. Experimental results reveal that character recognition accuracy reached 97.36% on engraved texts (Test Set A) and 95.71% on handwritten texts (Test Set C) when accounting for character variants. For layout recognition in complex layouts (Test Set B), the best team achieved a peak mean Average Precision (mAP) of 59.41% and an Intersection over Union (loU) of 76.38%. Our analysis indicates that calligraphic variability, layout density, and character variants significantly modulate system performance. Consequently, enhancing robustness within complex layouts and developing synergistic models that integrate textual and structural information remain primary challenges for intelligent interpretation of ancient writings .