Recognition of Oracle Bone Inscriptions by using Two Deep Learning Models

Fujikawa, Yoshiyuki; Li, Hengyi; Yue, Xuebin; Aravinda, C. V.; G, Amar Prabhu; Meng, Lin

doi:10.1007/s42803-022-00044-9

Cited by 18 publications

(7 citation statements)

References 17 publications

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“…According to previous research, you only look once (YOLO) object detection methods are used in [5], [6], which implements the CNN architecture. YOLOv3-tiny was able to recognise the Kawi character on copper inscriptions due to its high detection accuracy (average of 97.93% in [5] and high detection speed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…YOLOv3-tiny was able to recognise the Kawi character on copper inscriptions due to its high detection accuracy (average of 97.93% in [5] and high detection speed. Meanwhile, the Oracle Bone inscriptions (OBIs) were recognised using two deep learning models in [6]: first, YOLOv3-tiny was used to detect and recognise OBIs, and second, MobileNet was used to detect undetected OBIs, as YOLOv3-tiny's limitations prevent all OBIs from being correctly recognised. Thus, MobileNet had the best performance in training accuracy and validation accuracy (99.30% and 98.89%, respectively).…”

Section: Literature Reviewmentioning

confidence: 99%

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Recognition and Translation of Ancient South Arabian Musnad Inscriptions

Altalhi,

Alwethinani,

Alghamdi

et al. 2023

IJACSA

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Inscriptions play an important role in preserving historical information. As such, conservation of these inscriptions provides valuable insights into the history and cultural heritage of the region. Musnad inscriptions are considered one of the earliest forms of writing from the Arabian Peninsula, preceding the modern Arabic font; however, most Musnad inscriptions remain unread and untranslated, signifying a substantial loss of historical information. In response, this paper represents a significant contribution to the field by proposing a successful approach to interpreting Musnad inscriptions. To do so, a dataset was prepared from the Saudi Arabian Ministry of Culture and subjected to preprocessing for optimal recognition, a step that entailed several experiments to enhance image quality and preparedness for recognition. The dataset was then trained and tested with 29 classes using three different convolutional neural network (CNN) architectures: Visual Geometry Group 16 (VGG16), Residual Network 50 (ResNet50) and MobileNetV2. Thereafter, the performance of each architecture was evaluated based on its accuracy in recognising Musnad inscriptions. The results demonstrate that VGG16 achieved the highest accuracy of 93.81%, followed by ResNet50 at 89.39% and MobileNetV2 at 80.02%.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Recognition and Translation of Ancient South Arabian Musnad Inscriptions

Altalhi,

Alwethinani,

Alghamdi

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Zhang et al [13] adopted an improved Siamese network to learn the similarity between an oracle bone character and the corresponding template typeset images. Fujikawa et al [14] proposed a twostage method that adopts the latest You Only Look Once (YOLO) model and MobileNet for character recognition.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Neural Network Model Based on Inception-v3 for Oracle Bone Inscription Character Recognition

Guo

Zhou

Liu

et al. 2022

Scientific Programming

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Oracle bone inscription is the ancestor of modern Chinese characters. Character recognition is an essential part of the research of oracle bone inscription. In this paper, we propose an improved neural network model based on Inception-v3 for oracle bone inscription character recognition. We replace the original convolution block and add the Contextual Transformer block and the Convolutional Block Attention Module. We conduct character recognition experiments with the improved model on two oracle bone inscription character image datasets, HWOBC and OBC306, and the results indicate that the improved model can still achieve excellent results in the cases of blurred, occluded, and mutilated characters. We also select AlexNet, VGG-19, and Inception-v3 neural network models for the same experiments, and the comparison result shows that the proposed model outperforms other models in three evaluation indicators, namely, Top-1 Accuracy, Top-3 Accuracy, and Top-5 Accuracy, which indicate the correctness and excellence of our proposed model.

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“…Researchers are trying to reorganise these literature through image processing, deep learning, etc. Fujikawa et al., have designed a web application for assisting oracle bones inscriptions’ (OBIs) reorganisation [22]. OBI is a kind of hieroglyphics, which evaluated Chinese characters.…”

Section: Early Japanese Books and Related Workmentioning

confidence: 99%

The early Japanese books reorganization by combining image processing and deep learning

Tanaka

et al. 2022

CAAI Trans on Intel Tech

Self Cite

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Many early Japanese books record a large amount of information, including historical politics, economics, culture, and so on, which are all valuable legacies. These books are waiting to be reorganized at the moment. However, a large amount of the books are described by Kuzushiji, a type of handwriting cursive script that is no longer in use today and only readable by a few experts. Therefore, researchers are trying to detect and recognise the characters from these books through modern techniques. Unfortunately, the characteristics of the Kuzushiji, such as Connect‐Separate‐characters and Many‐variation, hinder the modern technique assisted re‐organisation. Connect‐Separate‐characters refer to the case of some characters connecting each other or one character being separated into unconnected parts, which makes character detection hard. Many‐variation is one of the typical characteristics of Kuzushiji, defined as the case that the same character has several variations even if they are written by the same person in the same book at the same time, which increases the difficulty of character recognition. In this sense, this paper aims to construct an early Japanese book reorganisation system by combining image processing and deep learning techniques. The experimentation has been done by testing two early Japanese books. In terms of character detection, the final Recall, Precision and F‐value reaches 79.8%, 80.3%, and 80.0%, respectively. The deep learning based character recognition accuracy of Top3 reaches 69.52%, and the highest recognition rate reaches 82.57%, which verifies the effectiveness of our proposal.

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Recognition of Oracle Bone Inscriptions by using Two Deep Learning Models

Cited by 18 publications

References 17 publications

Recognition and Translation of Ancient South Arabian Musnad Inscriptions

Recognition and Translation of Ancient South Arabian Musnad Inscriptions

An Improved Neural Network Model Based on Inception-v3 for Oracle Bone Inscription Character Recognition

The early Japanese books reorganization by combining image processing and deep learning

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