A Method of Japanese Ancient Text Recognition by Deep Learning

Chen, Lehan; Li, Bing; Tomiyama, Hiroyuki; Meng, Lin

doi:10.1016/j.procs.2020.06.084

Cited by 13 publications

(4 citation statements)

References 2 publications

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“…This body of work has focused on optical character recognition and visual analysis [31][32][33][34] , writer identification [35][36][37] and text analysis [38][39][40][41][42][43][44] , stylometrics 45 and document dating 46 . It is only very recently that scholarship has begun to use deep learning and neural networks for optical character recognition [47][48][49][50][51][52][53][54][55] , text analysis 56 , machine translation of ancient texts [57][58][59] , authorship attribution 60,61 and deciphering ancient languages 62,63 , and been applied to study the form and style of epigraphic monuments 64 .…”

Section: Previous Workmentioning

confidence: 99%

Restoring and attributing ancient texts using deep neural networks

Assael

Sommerschield

Shillingford

et al. 2022

Nature

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Ancient history relies on disciplines such as epigraphy—the study of inscribed texts known as inscriptions—for evidence of the thought, language, society and history of past civilizations1. However, over the centuries, many inscriptions have been damaged to the point of illegibility, transported far from their original location and their date of writing is steeped in uncertainty. Here we present Ithaca, a deep neural network for the textual restoration, geographical attribution and chronological attribution of ancient Greek inscriptions. Ithaca is designed to assist and expand the historian’s workflow. The architecture of Ithaca focuses on collaboration, decision support and interpretability. While Ithaca alone achieves 62% accuracy when restoring damaged texts, the use of Ithaca by historians improved their accuracy from 25% to 72%, confirming the synergistic effect of this research tool. Ithaca can attribute inscriptions to their original location with an accuracy of 71% and can date them to less than 30 years of their ground-truth ranges, redating key texts of Classical Athens and contributing to topical debates in ancient history. This research shows how models such as Ithaca can unlock the cooperative potential between artificial intelligence and historians, transformationally impacting the way that we study and write about one of the most important periods in human history.

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Section: Previous Workmentioning

confidence: 99%

Restoring and attributing ancient texts using deep neural networks

Assael

Sommerschield

Shillingford

et al. 2022

Nature

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“…After rapid development at home and abroad, ViT has also achieved good performance in computer vision tasks, such as detection [ 40 ], segmentation [ 41 ], tracking [ 42 ], image generation [ 43 ], enhancement [ 44 ], ancient text recognition [ 45 ], et al In the future, the ViT will have a broad development prospect.…”

Section: Related Workmentioning

confidence: 99%

PF-ViT: Parallel and Fast Vision Transformer for Offline Handwritten Chinese Character Recognition

Dan

Zhu

Jin

et al. 2022

Computational Intelligence and Neuroscience

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Recently, Vision Transformer (ViT) has been widely used in the field of image recognition. Unfortunately, the ViT model repeatedly stacks 12-layer encoders, resulting in a large number of model computations, many parameters, and slow training speed, making it difficult to deploy on mobile devices. In order to reduce the computational complexity of the model and improve the training speed, a parallel and fast Vision Transformer method for offline handwritten Chinese character recognition is proposed. The method adds parallel branches of the encoder module to the structure of the Vision Transformer model. Parallel modes include two-way parallel, four-way parallel, and seven-way parallel. The original picture is fed to the encoder module after flattening and linear embedding processing operations. The core step in the encoder is the multihead attention mechanism. Multihead self-attention can learn the interdependence between image sequence blocks. In addition, the use of data expansion strategies increases the diversity of data. In the two-way parallel experiment, when the model is 98.1% accurate on the dataset, the number of parameters and the number of FLOPs are 43.11 million and 4.32 G, respectively. Compared with the ViT model, whose parameters and FLOPs are 86 million and 16.8 G, respectively, the two-way parallel model has a 50.1% decrease in parameters and a 34.6% decrease in FLOPs. This method has been demonstrated to effectively reduce the computational complexity of the model while indirectly improving image recognition speed.

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“…However, Kuzushi-ji is not used in the present day, causing only few experts of classical Japanese can read Kuzushi-ji and understand the contents of the books. To re-organize and preserve this cultural heritage, researchers have digitalized the early Japanese books and applied the combining Kuzushi-ji and Optical Character Recognition(OCR) to recognize the Kuzushiji [1][2][3]. Humanities research intuition such as the Center for Open Data(CODH) [4] and Art Research Center(ARC) of Ritsumeikan [5] digitize the early Japanese books and re-organize them for the database to prevent degradation and prompt combine computer science with humanities.…”

Section: Introductionmentioning

confidence: 99%

Deteriorated Characters Restoration for Early Japanese Books Using Enhanced CycleGAN

2023

Self Cite

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Early Japanese books, classical humanities resources in Japan, have great historical and cultural value. However, Kuzushi-ji, the old character in early Japanese books, is scratched, faded ink, and lost due to weathering and deterioration over the years. The restoration of deteriorated early Japanese books has tremendous significance in cultural revitalization. In this paper, we introduce augmented identity loss and propose enhanced CycleGAN for deteriorated character restoration, which combines domain discriminators and augmented identity loss. This enhanced CycleGAN makes it possible to restore multiple levels of deterioration in the early Japanese books. It obtains the high readability of the actual deteriorated characters, which is proved by higher structural similarity(SSIM) and accuracy of deep learning models than standard CycleGAN and traditional image processing. In particular, SSIM increases by 8.72%, and the accuracy of ResNet50 for damaged characters improves by 1.1% compared with the competitive CycleGAN. Moreover, we realize the automatic restoration of pages of early Japanese books written about 300 years ago.

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A Method of Japanese Ancient Text Recognition by Deep Learning

Cited by 13 publications

References 2 publications

Restoring and attributing ancient texts using deep neural networks

Restoring and attributing ancient texts using deep neural networks

PF-ViT: Parallel and Fast Vision Transformer for Offline Handwritten Chinese Character Recognition

Deteriorated Characters Restoration for Early Japanese Books Using Enhanced CycleGAN

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