Seal-Robust KCR: A Robust Kuzushiji Character Recognition Framework under Seal Interference

Comparison between the proposed method and the conventional baseline on the synthetic test set. Higher values indicate better performance. The proposed method improves character detection using synthetic data augmentation and character classification through document restoration. End-to-end KCR evaluation recognizes text directly from document images.

Kuzushiji was one of the most widely used cursive writing systems in pre-modern Japan. Due to its highly cursive forms and extensive glyph variations, most modern Japanese readers are unable to read Kuzushiji characters. Consequently, recent studies have focused on developing automated Kuzushiji character recognition (KCR) methods, which have achieved strong performance on relatively clean Japanese historical document images. Although seals frequently appear in Japanese historical documents, existing methods often fail to maintain recognition accuracy under seal interference, particularly when seals overlap with characters. To address this challenge, we propose a seal-robust KCR framework. Based on character detection, classification, and ordering, the proposed framework additionally incorporates document restoration to mitigate seal interference, thereby improving overall recognition performance. In addition, we introduce a novel synthetic data augmentation strategy to enhance the performance of character detection models. We further correct annotation errors, reconstruct the dataset, and create a synthetic test set to simulate severe seal interference. Experimental results demonstrate the effectiveness of the proposed framework in mitigating the impact of seal interference on KCR. Compared with a conventional baseline and NDLkotenOCR, it achieves relative character error rate (CER) reductions of 39.7% and 5.9%, respectively, on the real test set, and 50.1% and 41.7%, respectively, on the synthetic test set.

Conventional pipeline (blue flow) and the proposed pipeline (red flow) for seal-interfered Japanese historical document images. Dashed arrows indicate additional processes performed in parallel with character detection without affecting the detection results.

Among the 1,000 document images, 267 were found to contain missing annotations. To improve annotation quality, the missing character bounding boxes were manually added with the assistance of a Kuzushiji expert. Red bounding boxes indicate the annotations newly added in this work, while green bounding boxes correspond to the original annotations.

We construct a synthetic test set to simulate severe seal interference. The seals shown in the top row originate from real historical documents, while those in the bottom row are synthetically overlaid.

Visual examples of detection results produced by the YOLO11-L model trained with the proposed Synthetic Data Augmentation (SDA) strategy. The top row shows low-confidence bounding boxes produced by the detection model. Stains in Japanese historical documents may cause false positives, resulting in background noise being mistakenly detected as Kuzushiji characters with confidence scores as low as 0.001. The bottom row shows the detection results with a confidence threshold of 0.1, which effectively removes most false positives.

Visual examples of document restoration results obtained using the proposed color-based thresholding algorithm with τ_r = 90 and (τ_rg, τ_rb) = (1.3, 1.3).

Visual examples of the input document images (top row) and the corresponding recognition results projected onto the original document images (bottom row). Despite the complex document layouts, the proposed visualization facilitates intuitive interpretation of historical documents by preserving the recognized characters within their original spatial context.