Unsupervised Structure-Texture Separation Network for Oracle Character Recognition

• URL: http://arxiv.org/abs/2205.06549v1
• Date: Fri, 13 May 2022 10:27:02 GMT
• Title: Unsupervised Structure-Texture Separation Network for Oracle Character Recognition
• Authors: Mei Wang, Weihong Deng, Cheng-Lin Liu
• Abstract summary: Oracle bone script is the earliest-known Chinese writing system of the Shang dynasty and is precious to archeology and philology. We propose a structure-texture separation network (STSN), which is an end-to-end learning framework for joint disentanglement, transformation, adaptation and recognition.
• Score: 70.29024469395608
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Oracle bone script is the earliest-known Chinese writing system of the Shang dynasty and is precious to archeology and philology. However, real-world scanned oracle data are rare and few experts are available for annotation which make the automatic recognition of scanned oracle characters become a challenging task. Therefore, we aim to explore unsupervised domain adaptation to transfer knowledge from handprinted oracle data, which are easy to acquire, to scanned domain. We propose a structure-texture separation network (STSN), which is an end-to-end learning framework for joint disentanglement, transformation, adaptation and recognition. First, STSN disentangles features into structure (glyph) and texture (noise) components by generative models, and then aligns handprinted and scanned data in structure feature space such that the negative influence caused by serious noises can be avoided when adapting. Second, transformation is achieved via swapping the learned textures across domains and a classifier for final classification is trained to predict the labels of the transformed scanned characters. This not only guarantees the absolute separation, but also enhances the discriminative ability of the learned features. Extensive experiments on Oracle-241 dataset show that STSN outperforms other adaptation methods and successfully improves recognition performance on scanned data even when they are contaminated by long burial and careless excavation.

Related papers

• Unsupervised Attention Regularization Based Domain Adaptation for Oracle Character Recognition [59.05212866862219]
  The study of oracle characters plays an important role in Chinese archaeology and philology. The difficulty of collecting and annotating real-world scanned oracle characters hinders the development of oracle character recognition. We develop a novel unsupervised domain adaptation (UDA) method to transfer recognition knowledge from labeled handprinted oracle characters to unlabeled scanned data.
  arXiv  Detail & Related papers  (2024-09-24T09:07:05Z)
• Oracle Character Recognition using Unsupervised Discriminative Consistency Network [65.64172835624206]
  We propose a novel unsupervised domain adaptation method for oracle character recognition (OrCR) We leverage pseudo-labeling to incorporate the semantic information into adaptation and constrain augmentation consistency. Our approach achieves state-of-the-art result on Oracle-241 dataset and substantially outperforms the recently proposed structure-texture separation network by 15.1%.
  arXiv  Detail & Related papers  (2023-12-11T02:52:27Z)
• Transformer-Based UNet with Multi-Headed Cross-Attention Skip Connections to Eliminate Artifacts in Scanned Documents [0.0]
  A modified UNet structure using a Swin Transformer backbone is presented to remove typical artifacts in scanned documents. An improvement in text extraction quality with a reduced error rate of up to 53.9% on the synthetic data is archived.
  arXiv  Detail & Related papers  (2023-06-05T12:12:23Z)
• Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline Performances [76.34037366117234]
  We introduce a new dataset called Robot Control Gestures (RoCoG-v2) The dataset is composed of both real and synthetic videos from seven gesture classes. We present results using state-of-the-art action recognition and domain adaptation algorithms.
  arXiv  Detail & Related papers  (2023-03-17T23:23:55Z)
• UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detection [52.91782218300844]
  We propose a novel Unsupervised Inconsistency-Aware method based on Vision Transformer, called UIA-ViT. Due to the self-attention mechanism, the attention map among patch embeddings naturally represents the consistency relation, making the vision Transformer suitable for the consistency representation learning.
  arXiv  Detail & Related papers  (2022-10-23T15:24:47Z)
• DFCANet: Dense Feature Calibration-Attention Guided Network for Cross Domain Iris Presentation Attack Detection [2.95102708174421]
  iris presentation attack detection (IPAD) is essential for securing personal identity. Existing IPAD algorithms do not generalize well to unseen and cross-domain scenarios. This paper proposes DFCANet: Dense Feature and Attention Guided Network.
  arXiv  Detail & Related papers  (2021-11-01T13:04:23Z)
• SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition [48.676064155070556]
  Arbitrary text appearance poses a great challenge in scene text recognition tasks. We introduce a new learnable geometric-unrelated module, the Structure-Preserving Inner Offset Network (SPIN) SPIN allows the color manipulation of source data within the network.
  arXiv  Detail & Related papers  (2020-05-27T01:47:07Z)

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.