LayeredDoc: Domain Adaptive Document Restoration with a Layer Separation Approach

• URL: http://arxiv.org/abs/2406.08610v1
• Date: Wed, 12 Jun 2024 19:41:01 GMT
• Title: LayeredDoc: Domain Adaptive Document Restoration with a Layer Separation Approach
• Authors: Maria Pilligua, Nil Biescas, Javier Vazquez-Corral, Josep Lladós, Ernest Valveny, Sanket Biswas,
• Abstract summary: This paper introduces a text-graphic layer separation approach that enhances domain adaptability in document image restoration systems. We propose LayeredDoc, which utilizes two layers of information: the first targets coarse-grained graphic components, while the second refines machine-printed textual content. We evaluate our approach both qualitatively and quantitatively using a new real-world dataset, LayeredDocDB, developed for this study.
• Score: 9.643486775455841
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The rapid evolution of intelligent document processing systems demands robust solutions that adapt to diverse domains without extensive retraining. Traditional methods often falter with variable document types, leading to poor performance. To overcome these limitations, this paper introduces a text-graphic layer separation approach that enhances domain adaptability in document image restoration (DIR) systems. We propose LayeredDoc, which utilizes two layers of information: the first targets coarse-grained graphic components, while the second refines machine-printed textual content. This hierarchical DIR framework dynamically adjusts to the characteristics of the input document, facilitating effective domain adaptation. We evaluated our approach both qualitatively and quantitatively using a new real-world dataset, LayeredDocDB, developed for this study. Initially trained on a synthetically generated dataset, our model demonstrates strong generalization capabilities for the DIR task, offering a promising solution for handling variability in real-world data. Our code is accessible on GitHub.

Related papers

• DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception [16.301481927603554]
  We introduce Doc-YOLO, a novel approach that enhances accuracy while maintaining speed advantages. For robust document pre-training, we introduce the Mesh-candidate BestFit algorithm. In terms of model optimization, we propose a Global-to-Local Controllable Receptive Module.
  arXiv  Detail & Related papers  (2024-10-16T14:50:47Z)
• VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents [66.42579289213941]
  Retrieval-augmented generation (RAG) is an effective technique that enables large language models to utilize external knowledge sources for generation. In this paper, we introduce VisRAG, which tackles this issue by establishing a vision-language model (VLM)-based RAG pipeline. In this pipeline, instead of first parsing the document to obtain text, the document is directly embedded using a VLM as an image and then retrieved to enhance the generation of a VLM.
  arXiv  Detail & Related papers  (2024-10-14T15:04:18Z)
• Contextual Document Embeddings [77.22328616983417]
  We propose two complementary methods for contextualized document embeddings. First, an alternative contrastive learning objective that explicitly incorporates the document neighbors into the intra-batch contextual loss. Second, a new contextual architecture that explicitly encodes neighbor document information into the encoded representation.
  arXiv  Detail & Related papers  (2024-10-03T14:33:34Z)
• READoc: A Unified Benchmark for Realistic Document Structured Extraction [44.44722729958791]
  We introduce a novel benchmark named READoc, which defines DSE as a realistic task. The READoc dataset is derived from 2,233 diverse and real-world documents from arXiv and GitHub. In addition, we develop a unified evaluation of state-of-the-art DSE approaches.
  arXiv  Detail & Related papers  (2024-09-08T15:42:48Z)
• HDT: Hierarchical Document Transformer [70.2271469410557]
  HDT exploits document structure by introducing auxiliary anchor tokens and redesigning the attention mechanism into a sparse multi-level hierarchy. We develop a novel sparse attention kernel that considers the hierarchical structure of documents.
  arXiv  Detail & Related papers  (2024-07-11T09:28:04Z)
• DECDM: Document Enhancement using Cycle-Consistent Diffusion Models [3.3813766129849845]
  We propose DECDM, an end-to-end document-level image translation method inspired by recent advances in diffusion models. Our method overcomes the limitations of paired training by independently training the source (noisy input) and target (clean output) models. We also introduce simple data augmentation strategies to improve character-glyph conservation during translation.
  arXiv  Detail & Related papers  (2023-11-16T07:16:02Z)
• GlobalDoc: A Cross-Modal Vision-Language Framework for Real-World Document Image Retrieval and Classification [8.880856137902947]
  We introduce GlobalDoc, a cross-modal transformer-based architecture pre-trained in a self-supervised manner. GlobalDoc improves the learning of richer semantic concepts by unifying language and visual representations. For proper evaluation, we also propose two novel document-level downstream VDU tasks, Few-Shot Document Image Classification (DIC) and Content-based Document Image Retrieval (DIR)
  arXiv  Detail & Related papers  (2023-09-11T18:35:14Z)
• HRDoc: Dataset and Baseline Method Toward Hierarchical Reconstruction of Document Structures [31.868926876151342]
  This paper introduces hierarchical reconstruction of document structures as a novel task suitable for NLP and CV fields. We built a large-scale dataset named HRDoc, which consists of 2,500 multi-page documents with nearly 2 million semantic units. We propose an encoder-decoder-based hierarchical document structure parsing system (DSPS) to tackle this problem.
  arXiv  Detail & Related papers  (2023-03-24T07:23:56Z)
• Unifying Vision, Text, and Layout for Universal Document Processing [105.36490575974028]
  We propose a Document AI model which unifies text, image, and layout modalities together with varied task formats, including document understanding and generation. Our method sets the state-of-the-art on 9 Document AI tasks, e.g., document understanding and QA, across diverse data domains like finance reports, academic papers, and websites.
  arXiv  Detail & Related papers  (2022-12-05T22:14:49Z)
• One-shot Key Information Extraction from Document with Deep Partial Graph Matching [60.48651298832829]
  Key Information Extraction (KIE) from documents improves efficiency, productivity, and security in many industrial scenarios. Existing supervised learning methods for the KIE task need to feed a large number of labeled samples and learn separate models for different types of documents. We propose a deep end-to-end trainable network for one-shot KIE using partial graph matching.
  arXiv  Detail & Related papers  (2021-09-26T07:45:53Z)
• Hybrid Generative-Retrieval Transformers for Dialogue Domain Adaptation [77.62366712130196]
  We present the winning entry at the fast domain adaptation task of DSTC8, a hybrid generative-retrieval model based on GPT-2 fine-tuned to the multi-domain MetaLWOz dataset. Our model uses retrieval logic as a fallback, being SoTA on MetaLWOz in human evaluation (>4% improvement over the 2nd place system) and attaining competitive generalization performance in adaptation to the unseen MultiWOZ dataset.
  arXiv  Detail & Related papers  (2020-03-03T18:07:42Z)

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.