docExtractor: An off-the-shelf historical document element extraction

• URL: http://arxiv.org/abs/2012.08191v1
• Date: Tue, 15 Dec 2020 10:19:18 GMT
• Title: docExtractor: An off-the-shelf historical document element extraction
• Authors: Tom Monnier, Mathieu Aubry
• Abstract summary: We present docExtractor, a generic approach for extracting visual elements such as text lines or illustrations from historical documents. We demonstrate it provides high-quality performances as an off-the-shelf system across a wide variety of datasets. We introduce a new public dataset dubbed IlluHisDoc dedicated to the fine evaluation of illustration segmentation in historical documents.
• Score: 18.828438308738495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present docExtractor, a generic approach for extracting visual elements such as text lines or illustrations from historical documents without requiring any real data annotation. We demonstrate it provides high-quality performances as an off-the-shelf system across a wide variety of datasets and leads to results on par with state-of-the-art when fine-tuned. We argue that the performance obtained without fine-tuning on a specific dataset is critical for applications, in particular in digital humanities, and that the line-level page segmentation we address is the most relevant for a general purpose element extraction engine. We rely on a fast generator of rich synthetic documents and design a fully convolutional network, which we show to generalize better than a detection-based approach. Furthermore, we introduce a new public dataset dubbed IlluHisDoc dedicated to the fine evaluation of illustration segmentation in historical documents.

Related papers

• 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)
• SynthDoc: Bilingual Documents Synthesis for Visual Document Understanding [23.910783272007407]
  This paper introduces SynthDoc, a novel synthetic document generation pipeline designed to enhance Visual Document Understanding (VDU) Addressing the challenges of data acquisition and the limitations of existing datasets, SynthDoc leverages publicly available corpora and advanced rendering tools to create a comprehensive and versatile dataset. Our experiments, conducted using the Donut model, demonstrate that models trained with SynthDoc's data achieve superior performance in pre-training read tasks and maintain robustness in downstream tasks, despite language inconsistencies.
  arXiv  Detail & Related papers  (2024-08-27T03:31:24Z)
• Leveraging Contextual Information for Effective Entity Salience Detection [21.30389576465761]
  We show that fine-tuning medium-sized language models with a cross-encoder style architecture yields substantial performance gains over feature engineering approaches. We also show that zero-shot prompting of instruction-tuned language models yields inferior results, indicating the task's uniqueness and complexity.
  arXiv  Detail & Related papers  (2023-09-14T19:04:40Z)
• Prompt me a Dataset: An investigation of text-image prompting for historical image dataset creation using foundation models [0.9065034043031668]
  We present a pipeline for image extraction from historical documents using foundation models. We evaluate text-image prompts and their effectiveness on humanities datasets of varying levels of complexity.
  arXiv  Detail & Related papers  (2023-09-04T15:37:03Z)
• SelfDocSeg: A Self-Supervised vision-based Approach towards Document Segmentation [15.953725529361874]
  Document layout analysis is a known problem to the documents research community. With growing internet connectivity to personal life, an enormous amount of documents had been available in the public domain. We address this challenge using self-supervision and unlike, the few existing self-supervised document segmentation approaches.
  arXiv  Detail & Related papers  (2023-05-01T12:47:55Z)
• Modeling Entities as Semantic Points for Visual Information Extraction in the Wild [55.91783742370978]
  We propose an alternative approach to precisely and robustly extract key information from document images. We explicitly model entities as semantic points, i.e., center points of entities are enriched with semantic information describing the attributes and relationships of different entities. The proposed method can achieve significantly enhanced performance on entity labeling and linking, compared with previous state-of-the-art models.
  arXiv  Detail & Related papers  (2023-03-23T08:21:16Z)
• TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents [51.744527199305445]
  This paper proposes a unified end-to-end information extraction framework from visually rich documents. Text reading and information extraction can reinforce each other via a well-designed multi-modal context block. The framework can be trained in an end-to-end trainable manner, achieving global optimization.
  arXiv  Detail & Related papers  (2022-07-14T08:52:07Z)
• Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration [75.47708732473586]
  We propose a layout-aware document-level Information Extraction dataset, LIE, to facilitate the study of extracting both structural and semantic knowledge from visually rich documents. LIE contains 62k annotations of three extraction tasks from 4,061 pages in product and official documents. Empirical results show that layout is critical for VRD-based extraction, and system demonstration also verifies that the extracted knowledge can help locate the answers that users care about.
  arXiv  Detail & Related papers  (2022-07-14T07:59:45Z)
• iFacetSum: Coreference-based Interactive Faceted Summarization for Multi-Document Exploration [63.272359227081836]
  iFacetSum integrates interactive summarization together with faceted search. Fine-grained facets are automatically produced based on cross-document coreference pipelines.
  arXiv  Detail & Related papers  (2021-09-23T20:01:11Z)
• Generating Synthetic Handwritten Historical Documents With OCR Constrained GANs [2.3808546906079178]
  We present a framework to generate synthetic historical documents with precise ground truth using nothing more than a collection of unlabeled historical images. We demonstrate a high-quality synthesis that makes it possible to generate large labeled historical document datasets with precise ground truth.
  arXiv  Detail & Related papers  (2021-03-15T09:39:17Z)
• SciREX: A Challenge Dataset for Document-Level Information Extraction [56.83748634747753]
  It is challenging to create a large-scale information extraction dataset at the document level. We introduce SciREX, a document level IE dataset that encompasses multiple IE tasks. We develop a neural model as a strong baseline that extends previous state-of-the-art IE models to document-level IE.
  arXiv  Detail & Related papers  (2020-05-01T17:30:10Z)

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.