17,051 research outputs found
Image Segmentation methods for fine-grained OCR Document Layout Analysis
Digitization has changed history research. The materials are available, and online archives make it easier to find the correct information and speed up the search for information. The remaining challenge is how to use modern digital methods to analyze the text of historical documents in more detail. This is an active research topic in digital humanities and computer science areas.
Document layout analysis is where computer vision object detection methods can be applied to historical documents to identify the document pages’ present objects (i.e., page elements). The recent development in deep learning based computer vision provides excellent tools for this purpose. However, most reviewed systems focus on coarse-grained methods, where only the high-level page elements are detected (e.g., text, figures, tables). Fine-grained detection methods are required
to be able to analyze texts on a more detailed level; for example, footnotes and marginalia are distinguished from the body text to enable proper analysis.
The thesis studies how image segmentation techniques can be used for fine-grained OCR document layout analysis. How to implement fine-grained page segmentation and region classification systems in practice, and what are the accuracy and the main challenges of such a system? The thesis includes implementing a layout analysis model that uses the instance segmentation method (Mask R-CNN). This implementation is compared against another existing layout analysis using the semantic segmentation method (U-net based P2PaLA implementation)
Adaptive Methods for Robust Document Image Understanding
A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current defficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity- and threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy
Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers
The massive amounts of digitized historical documents acquired over the last
decades naturally lend themselves to automatic processing and exploration.
Research work seeking to automatically process facsimiles and extract
information thereby are multiplying with, as a first essential step, document
layout analysis. If the identification and categorization of segments of
interest in document images have seen significant progress over the last years
thanks to deep learning techniques, many challenges remain with, among others,
the use of finer-grained segmentation typologies and the consideration of
complex, heterogeneous documents such as historical newspapers. Besides, most
approaches consider visual features only, ignoring textual signal. In this
context, we introduce a multimodal approach for the semantic segmentation of
historical newspapers that combines visual and textual features. Based on a
series of experiments on diachronic Swiss and Luxembourgish newspapers, we
investigate, among others, the predictive power of visual and textual features
and their capacity to generalize across time and sources. Results show
consistent improvement of multimodal models in comparison to a strong visual
baseline, as well as better robustness to high material variance
Logical segmentation for article extraction in digitized old newspapers
Newspapers are documents made of news item and informative articles. They are
not meant to be red iteratively: the reader can pick his items in any order he
fancies. Ignoring this structural property, most digitized newspaper archives
only offer access by issue or at best by page to their content. We have built a
digitization workflow that automatically extracts newspaper articles from
images, which allows indexing and retrieval of information at the article
level. Our back-end system extracts the logical structure of the page to
produce the informative units: the articles. Each image is labelled at the
pixel level, through a machine learning based method, then the page logical
structure is constructed up from there by the detection of structuring entities
such as horizontal and vertical separators, titles and text lines. This logical
structure is stored in a METS wrapper associated to the ALTO file produced by
the system including the OCRed text. Our front-end system provides a web high
definition visualisation of images, textual indexing and retrieval facilities,
searching and reading at the article level. Articles transcriptions can be
collaboratively corrected, which as a consequence allows for better indexing.
We are currently testing our system on the archives of the Journal de Rouen,
one of France eldest local newspaper. These 250 years of publication amount to
300 000 pages of very variable image quality and layout complexity. Test year
1808 can be consulted at plair.univ-rouen.fr.Comment: ACM Document Engineering, France (2012
A survey of comics research in computer science
Graphical novels such as comics and mangas are well known all over the world.
The digital transition started to change the way people are reading comics,
more and more on smartphones and tablets and less and less on paper. In the
recent years, a wide variety of research about comics has been proposed and
might change the way comics are created, distributed and read in future years.
Early work focuses on low level document image analysis: indeed comic books are
complex, they contains text, drawings, balloon, panels, onomatopoeia, etc.
Different fields of computer science covered research about user interaction
and content generation such as multimedia, artificial intelligence,
human-computer interaction, etc. with different sets of values. We propose in
this paper to review the previous research about comics in computer science, to
state what have been done and to give some insights about the main outlooks
Semantics-Based Content Extraction in Typewritten Historical Documents
This paper presents a flexible approach to extracting content from scanned historical documents using semantic information. The final electronic document is the result of a "digital historical document lifecycle" process, where the expert knowledge of the historian/archivist user is incorporated at different stages. Results show that such a conversion strategy aided by (expert) user-specified semantic information and which enables the processing of individual parts of the document in a specialised way, produces superior (in a variety of significant ways) results than document analysis and understanding techniques devised for contemporary documents
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