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Kuzushiji character recognition (KCR) methods, which have achieved strong performance on relatively clean Japanese historical document images. Although seals frequently appear in Japanese historical documents, existing methods often fail to maintain recognition accuracy under seal interference, particularly when seals overlap with characters.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.