Recognition of Oracle Bone Inscriptions by using Two Deep Learning Models

• URL: http://arxiv.org/abs/2105.00777v2
• Date: Tue, 4 May 2021 05:23:14 GMT
• Title: Recognition of Oracle Bone Inscriptions by using Two Deep Learning Models
• Authors: Yoshiyuki Fujikawa, Hengyi Li, Xuebin Yue, Aravinda C V, Amar Prabhu G, Lin Meng
• Abstract summary: Oracle bone inscriptions (OBIs) contain some of the oldest characters in the world and were used in China about 3000 years ago. This paper aims to design a online OBI recognition system for helping preservation and organization the cultural heritage.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Oracle bone inscriptions (OBIs) contain some of the oldest characters in the world and were used in China about 3000 years ago. As an ancient form of literature, OBIs store a lot of information that can help us understand the world history, character evaluations, and more. However, as OBIs were found only discovered about 120 years ago, few studies have described them, and the aging process has made the inscriptions less legible. Hence, automatic character detection and recognition has become an important issue. This paper aims to design a online OBI recognition system for helping preservation and organization the cultural heritage. We evaluated two deep learning models for OBI recognition, and have designed an API that can be accessed online for OBI recognition. In the first stage, you only look once (YOLO) is applied for detecting and recognizing OBIs. However, not all of the OBIs can be detected correctly by YOLO, so we next utilize MobileNet to recognize the undetected OBIs by manually cropping the undetected OBI in the image. MobileNet is used for this second stage of recognition as our evaluation of ten state-of-the-art models showed that it is the best network for OBI recognition due to its superior performance in terms of accuracy, loss and time consumption. We installed our system on an application programming interface (API) and opened it for OBI detection and recognition.

Related papers

• OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptions [7.657419462547438]
  Oracle bone inscriptions (OBIs) are the earliest known form of Chinese characters and serve as a valuable resource for research in anthropology and archaeology. Previous methods either focus on pixel-level information or utilize vanilla transformers for glyph-based OBI denoising. This paper proposes a fast attentive denoising framework for oracle bone inscriptions, i.e., OBIFormer.
  arXiv  Detail & Related papers  (2025-04-18T07:24:35Z)
• Mitigating Long-tail Distribution in Oracle Bone Inscriptions: Dataset, Model, and Benchmark [36.21507457913964]
  oracle bone inscription (OBI) recognition plays a significant role in understanding the history and culture of ancient China. The existing OBI datasets suffer from a long-tail distribution problem, leading to biased performance of OBI recognition models across majority and minority classes. We present the Oracle-P15K, a structure-aligned OBI dataset for OBI generation and denoising, consisting of 14,542 images infused with domain knowledge from OBI experts.
  arXiv  Detail & Related papers  (2025-04-13T13:03:25Z)
• OBI-Bench: Can LMMs Aid in Study of Ancient Script on Oracle Bones? [40.226986425846825]
  We introduce OBI-Bench, a holistic benchmark crafted to evaluate large multi-modal models (LMMs) on whole-process oracle bone inscriptions. OBI-Bench includes 5,523 meticulously collected diverse-sourced images, covering five key domain problems: recognition, rejoining, classification, retrieval, and deciphering. Unlike existing benchmarks, OBI-Bench focuses on advanced visual perception and reasoning with OBI-specific knowledge, challenging LMMs to perform tasks akin to those faced by experts.
  arXiv  Detail & Related papers  (2024-12-02T06:31:28Z)
• Learning based Ge'ez character handwritten recognition [7.699119649521884]
  Ge'ez, an ancient Ethiopic script of cultural and historical significance, has been largely neglected in handwriting recognition research. We develop a state-of-the-art Ge'ez handwriting recognition system using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Our approach uses a two-stage recognition process. First, a CNN is trained to recognize individual characters, which then acts as a feature extractor for an LSTM-based system for word recognition.
  arXiv  Detail & Related papers  (2024-11-20T14:22:15Z)
• Oracle Bone Inscriptions Multi-modal Dataset [58.20314888996118]
  Oracle bone inscriptions(OBI) is the earliest developed writing system in China, bearing invaluable written exemplifications of early Shang history and paleography. This paper proposes an Oracle Bone Inscriptions Multi-modal dataset, which includes annotation information for 10,077 pieces of oracle bones. This dataset can be used for a variety of AI-related research tasks relevant to the field of OBI, such as OBI Character Detection and Recognition, Rubbing Denoising, Character Matching, Character Generation, Reading Sequence Prediction, Missing Characters Completion task and so on.
  arXiv  Detail & Related papers  (2024-07-04T12:47:32Z)
• Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection [71.93411099797308]
  Out-of-distribution (OOD) samples are crucial when deploying machine learning models in open-world scenarios. We propose to tackle this constraint by leveraging the expert knowledge and reasoning capability of large language models (LLM) to potential Outlier Exposure, termed EOE. EOE can be generalized to different tasks, including far, near, and fine-language OOD detection. EOE achieves state-of-the-art performance across different OOD tasks and can be effectively scaled to the ImageNet-1K dataset.
  arXiv  Detail & Related papers  (2024-06-02T17:09:48Z)
• Deciphering Oracle Bone Language with Diffusion Models [70.69739681961558]
  Oracle Bone Script (OBS) originated from China's Shang Dynasty approximately 3,000 years ago. This paper introduces a novel approach by adopting image generation techniques, specifically through the development of Oracle Bone Script Decipher (OBSD) OBSD generates vital clues for decipherment, charting a new course for AI-assisted analysis of ancient languages.
  arXiv  Detail & Related papers  (2024-06-02T09:42:23Z)
• Arabic Handwritten Text for Person Biometric Identification: A Deep Learning Approach [0.9910347287556193]
  This study thoroughly investigates how well deep learning models can recognize Arabic handwritten text for person biometric identification. It compares three advanced architectures -- ResNet50, MobileNetV2, and EfficientNetB7 -- using three widely recognized datasets. Results show that EfficientNetB7 outperforms the others, achieving test accuracies of 98.57%, 99.15%, and 99.79% on AHAWP, Khatt, and LAMIS-MSHD datasets.
  arXiv  Detail & Related papers  (2024-06-01T11:43:00Z)
• Deep learning for table detection and structure recognition: A survey [49.09628624903334]
  The goal of this survey is to provide a profound comprehension of the major developments in the field of Table Detection. We provide an analysis of both classic and new applications in the field. The datasets and source code of the existing models are organized to provide the reader with a compass on this vast literature.
  arXiv  Detail & Related papers  (2022-11-15T19:42:27Z)
• Deep Learning Architecture for Automatic Essay Scoring [0.0]
  We propose a novel architecture based on recurrent networks (RNN) and convolution neural network (CNN) In the proposed architecture, the multichannel convolutional layer learns and captures the contextual features of the word n-gram from the word embedding vectors. Our proposed system achieves significantly higher grading accuracy than other deep learning-based AES systems.
  arXiv  Detail & Related papers  (2022-06-16T14:56:24Z)
• Deep Learning for Face Anti-Spoofing: A Survey [74.42603610773931]
  Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs)
  arXiv  Detail & Related papers  (2021-06-28T19:12:00Z)
• Towards Palmprint Verification On Smartphones [62.279124220123286]
  Studies in the past two decades have shown that palmprints have outstanding merits in uniqueness and permanence. We built a DCNN-based palmprint verification system named DeepMPV+ for smartphones. The efficiency and efficacy of DeepMPV+ have been corroborated by extensive experiments.
  arXiv  Detail & Related papers  (2020-03-30T08:31:03Z)
• DeepSign: Deep On-Line Signature Verification [6.5379404287240295]
  This study provides an in-depth analysis of state-of-the-art deep learning approaches for on-line signature verification. We present and describe the new DeepSignDB on-line handwritten signature biometric public database. We adapt and evaluate our recent deep learning approach named Time-Aligned Recurrent Neural Networks (TA-RNNs) for the task of on-line handwritten signature verification.
  arXiv  Detail & Related papers  (2020-02-24T08:53:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.