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Attempts to recognize anomalously deformed Kana in Japanese historical documents.

, , , , and . HIP@ICDAR, page 31-36. ACM, (2017)

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Nom document digitalization by deep convolution neural networks., , and . Pattern Recognit. Lett., (2020)A High Accuracy Text Detection Model of Newly Constructing and Training Strategies., and . ICPRAM, page 635-642. SCITEPRESS, (2021)Incremental Teacher Model with Mixed Augmentations and Scheduled Pseudo-label Loss for Handwritten Text Recognition., , , , , , and . ICDAR (4), volume 14190 of Lecture Notes in Computer Science, page 287-301. Springer, (2023)Deep Convolutional Recurrent Network for Segmentation-Free Offline Handwritten Japanese Text Recognition., , , and . MOCR@ICDAR, page 5-9. IEEE, (2017)978-1-5386-3586-5.Tens of Thousands of Nom Character Recognition by Deep Convolution Neural Networks., , and . HIP@ICDAR, page 37-41. ACM, (2017)Enhanced Character Segmentation for Format-Free Japanese Text Recognition., and . ICFHR, page 138-143. IEEE Computer Society, (2016)A Character Attention Generative Adversarial Network for Degraded Historical Document Restoration., , , and . ICDAR, page 420-425. IEEE, (2019)A Semantic Segmentation-based Method for Handwritten Japanese Text Recognition., , and . ICFHR, page 127-132. IEEE, (2020)Attempts to recognize anomalously deformed Kana in Japanese historical documents., , , , and . HIP@ICDAR, page 31-36. ACM, (2017)