Variational Augmentation for Enhancing Historical Document Image Binarization

• URL: http://arxiv.org/abs/2211.06581v1
• Date: Sat, 12 Nov 2022 06:01:21 GMT
• Title: Variational Augmentation for Enhancing Historical Document Image Binarization
• Authors: Avirup Dey, Nibaran Das, Mita Nasipuri
• Abstract summary: Historical Document Image Binarization is a well-known segmentation problem in image processing. We have proposed a novel two-stage framework -- the first of which comprises a generator that generates degraded samples using variational inference. The second is a CNN-based binarization network that trains on the generated data.
• Score: 11.342730352935913
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Historical Document Image Binarization is a well-known segmentation problem in image processing. Despite ubiquity, traditional thresholding algorithms achieved limited success on severely degraded document images. With the advent of deep learning, several segmentation models were proposed that made significant progress in the field but were limited by the unavailability of large training datasets. To mitigate this problem, we have proposed a novel two-stage framework -- the first of which comprises a generator that generates degraded samples using variational inference and the second being a CNN-based binarization network that trains on the generated data. We evaluated our framework on a range of DIBCO datasets, where it achieved competitive results against previous state-of-the-art methods.

Related papers

• Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Data [66.49494950674402]
  We leverage emerging text-to-image generative models in creating large-scale synthetic supervision for the task of damage assessment from aerial images. We build an efficient and easily scalable pipeline to generate thousands of post-disaster images from low-resource domains. We validate the strength of our proposed framework under cross-geography domain transfer setting from xBD and SKAI images in both single-source and multi-source settings.
  arXiv  Detail & Related papers  (2024-05-22T16:07:05Z)
• Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model [80.61157097223058]
  A prevalent strategy to bolster image classification performance is through augmenting the training set with synthetic images generated by T2I models. In this study, we scrutinize the shortcomings of both current generative and conventional data augmentation techniques. We introduce an innovative inter-class data augmentation method known as Diff-Mix, which enriches the dataset by performing image translations between classes.
  arXiv  Detail & Related papers  (2024-03-28T17:23:45Z)
• A Fair Evaluation of Various Deep Learning-Based Document Image Binarization Approaches [5.393847875065119]
  Binarization of document images is an important pre-processing step in the field of document analysis. Deep learning techniques are able to generate binarized versions of the images by learning context-dependent features. This work focuses on the evaluation of different deep learning-based methods under the same evaluation protocol.
  arXiv  Detail & Related papers  (2024-01-22T10:42:51Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• Feature transforms for image data augmentation [74.12025519234153]
  In image classification, many augmentation approaches utilize simple image manipulation algorithms. In this work, we build ensembles on the data level by adding images generated by combining fourteen augmentation approaches. Pretrained ResNet50 networks are finetuned on training sets that include images derived from each augmentation method.
  arXiv  Detail & Related papers  (2022-01-24T14:12:29Z)
• Two-stage generative adversarial networks for document image binarization with color noise and background removal [7.639067237772286]
  We propose a two-stage color document image enhancement and binarization method using generative adversarial neural networks. In the first stage, four color-independent adversarial networks are trained to extract color foreground information from an input image. In the second stage, two independent adversarial networks with global and local features are trained for image binarization of documents of variable size.
  arXiv  Detail & Related papers  (2020-10-20T07:51:50Z)
• Quantifying Model Uncertainty in Inverse Problems via Bayesian Deep Gradient Descent [4.029853654012035]
  Recent advances in inverse problems leverage powerful data-driven models, e.g., deep neural networks. We develop a scalable, data-driven, knowledge-aided computational framework to quantify the model uncertainty via Bayesian neural networks.
  arXiv  Detail & Related papers  (2020-07-20T09:43:31Z)
• Transformation Consistency Regularization- A Semi-Supervised Paradigm for Image-to-Image Translation [18.870983535180457]
  We propose Transformation Consistency Regularization, which delves into a more challenging setting of image-to-image translation. We evaluate the efficacy of our algorithm on three different applications: image colorization, denoising and super-resolution. Our method is significantly data efficient, requiring only around 10 - 20% of labeled samples to achieve similar image reconstructions to its fully-supervised counterpart.
  arXiv  Detail & Related papers  (2020-07-15T17:41:35Z)
• One-Shot Object Detection without Fine-Tuning [62.39210447209698]
  We introduce a two-stage model consisting of a first stage Matching-FCOS network and a second stage Structure-Aware Relation Module. We also propose novel training strategies that effectively improve detection performance. Our method exceeds the state-of-the-art one-shot performance consistently on multiple datasets.
  arXiv  Detail & Related papers  (2020-05-08T01:59:23Z)
• Seismic horizon detection with neural networks [62.997667081978825]
  This paper is an open-sourced research of applying binary segmentation approach to the task of horizon detection on multiple real seismic cubes with a focus on inter-cube generalization of the predictive model. The main contribution of this paper is an open-sourced research of applying binary segmentation approach to the task of horizon detection on multiple real seismic cubes with a focus on inter-cube generalization of the predictive model.
  arXiv  Detail & Related papers  (2020-01-10T11:30:50Z)

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.