Unsupervised Deep Learning for Handwritten Page Segmentation

• URL: http://arxiv.org/abs/2101.07487v1
• Date: Tue, 19 Jan 2021 07:13:38 GMT
• Title: Unsupervised Deep Learning for Handwritten Page Segmentation
• Authors: Ahmad Droby, Berat Kurar Barakat, Borak Madi, Reem Alaasam and Jihad El-Sana
• Abstract summary: We present an unsupervised deep learning method for page segmentation. A siamese neural network is trained to differentiate between patches using their measurable properties. Our experiments show that the proposed unsupervised method is as effective as typical supervised methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Segmenting handwritten document images into regions with homogeneous patterns is an important pre-processing step for many document images analysis tasks. Hand-labeling data to train a deep learning model for layout analysis requires significant human effort. In this paper, we present an unsupervised deep learning method for page segmentation, which revokes the need for annotated images. A siamese neural network is trained to differentiate between patches using their measurable properties such as number of foreground pixels, and average component height and width. The network is trained that spatially nearby patches are similar. The network's learned features are used for page segmentation, where patches are classified as main and side text based on the extracted features. We tested the method on a dataset of handwritten document images with quite complex layouts. Our experiments show that the proposed unsupervised method is as effective as typical supervised methods.

Related papers

• Learning Semantic Segmentation with Query Points Supervision on Aerial Images [57.09251327650334]
  We present a weakly supervised learning algorithm to train semantic segmentation algorithms. Our proposed approach performs accurate semantic segmentation and improves efficiency by significantly reducing the cost and time required for manual annotation.
  arXiv  Detail & Related papers  (2023-09-11T14:32:04Z)
• Shatter and Gather: Learning Referring Image Segmentation with Text Supervision [52.46081425504072]
  We present a new model that discovers semantic entities in input image and then combines such entities relevant to text query to predict the mask of the referent. Our method was evaluated on four public benchmarks for referring image segmentation, where it clearly outperformed the existing method for the same task and recent open-vocabulary segmentation models on all the benchmarks.
  arXiv  Detail & Related papers  (2023-08-29T15:39:15Z)
• Unsupervised Segmentation of Fetal Brain MRI using Deep Learning Cascaded Registration [2.494736313545503]
  Traditional deep learning-based automatic segmentation requires extensive training data with ground-truth labels. We propose a novel method based on multi-atlas segmentation, that accurately segments multiple tissues without relying on labeled data for training. Our method employs a cascaded deep learning network for 3D image registration, which computes small, incremental deformations to the moving image to align it precisely with the fixed image.
  arXiv  Detail & Related papers  (2023-07-07T13:17:12Z)
• Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation [18.755759024796216]
  Deep neural networks have greatly advanced histopathology image segmentation but usually require abundant data. We present a label-efficient tissue prototype dictionary building pipeline and propose to use the obtained prototypes to guide histopathology image segmentation. We show that our human-machine interactive tissue prototype learning method can achieve comparable segmentation performance as the fully-supervised baselines.
  arXiv  Detail & Related papers  (2022-11-26T06:17:21Z)
• Sketch-Guided Text-to-Image Diffusion Models [57.12095262189362]
  We introduce a universal approach to guide a pretrained text-to-image diffusion model. Our method does not require to train a dedicated model or a specialized encoder for the task. We take a particular focus on the sketch-to-image translation task, revealing a robust and expressive way to generate images.
  arXiv  Detail & Related papers  (2022-11-24T18:45:32Z)
• Learning to Annotate Part Segmentation with Gradient Matching [58.100715754135685]
  This paper focuses on tackling semi-supervised part segmentation tasks by generating high-quality images with a pre-trained GAN. In particular, we formulate the annotator learning as a learning-to-learn problem. We show that our method can learn annotators from a broad range of labelled images including real images, generated images, and even analytically rendered images.
  arXiv  Detail & Related papers  (2022-11-06T01:29:22Z)
• Unsupervised learning of text line segmentation by differentiating coarse patterns [0.0]
  We present an unsupervised deep learning method that embeds document image patches to a compact Euclidean space where distances correspond to a coarse text line pattern similarity. Text line segmentation can be easily implemented using standard techniques with the embedded feature vectors. We evaluate the method qualitatively and quantitatively on several variants of text line segmentation datasets to demonstrate its effectivity.
  arXiv  Detail & Related papers  (2021-05-19T21:21:30Z)
• Multiple Document Datasets Pre-training Improves Text Line Detection With Deep Neural Networks [2.5352713493505785]
  We introduce a fully convolutional network for the document layout analysis task. Our method Doc-UFCN relies on a U-shaped model trained from scratch for detecting objects from historical documents. We show that Doc-UFCN outperforms state-of-the-art methods on various datasets.
  arXiv  Detail & Related papers  (2020-12-28T09:48:33Z)
• Region Comparison Network for Interpretable Few-shot Image Classification [97.97902360117368]
  Few-shot image classification has been proposed to effectively use only a limited number of labeled examples to train models for new classes. We propose a metric learning based method named Region Comparison Network (RCN), which is able to reveal how few-shot learning works. We also present a new way to generalize the interpretability from the level of tasks to categories.
  arXiv  Detail & Related papers  (2020-09-08T07:29:05Z)
• Few-Shot Semantic Segmentation Augmented with Image-Level Weak Annotations [23.02986307143718]
  Recent progress in fewshot semantic segmentation tackles the issue by only a few pixel-level annotated examples. Our key idea is to learn a better prototype representation of the class by fusing the knowledge from the image-level labeled data. We propose a new framework, called PAIA, to learn the class prototype representation in a metric space by integrating image-level annotations.
  arXiv  Detail & Related papers  (2020-07-03T04:58:20Z)
• Distilling Localization for Self-Supervised Representation Learning [82.79808902674282]
  Contrastive learning has revolutionized unsupervised representation learning. Current contrastive models are ineffective at localizing the foreground object. We propose a data-driven approach for learning in variance to backgrounds.
  arXiv  Detail & Related papers  (2020-04-14T16:29:42Z)

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.