Survey and empirical comparison of different approaches for text extraction from scholarly figures View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-11

AUTHORS

Falk Böschen, Tilman Beck, Ansgar Scherp

ABSTRACT

Different approaches have been proposed in the past to address the challenge of extracting text from scholarly figures. However, until recently, no comparative evaluation of the different approaches had been conducted. Thus, we performed an extensive study of the related work and evaluated in total 32 different approaches. In this work, we perform a more detailed comparison of the 7 most relevant approaches described in the literature and extend to 37 systematic linear combinations of methods for extracting text from scholarly figures. Our generic pipeline, consisting of six steps, allows us to freely combine the different possible methods and perform a fair comparison. Overall, we have evaluated 44 different linear pipeline configurations and systematically compared the different methods. We then derived two non-linear configurations and a two-pass approach. We evaluate all pipeline configurations over four datasets of scholarly figures of different origin and characteristics. The quality of the extraction results is assessed using F-measure and Levenshtein distance, and we measure the runtime performance. Our experiments showed that there is a linear configuration that overall shows the best text extraction quality on all datasets. Further experiments showed that the best configuration can be improved by extending it to a two-pass approach. Regarding the runtime, we observed huge differences from very fast approaches to those running for several weeks. Our experiments found the best working configuration for text extraction from our method set. However, they also showed that further improvements regarding region extraction and classification are needed. More... »

PAGES

29475-29505

References to SciGraph publications

  • 2013. Three-Stage Method of Text Region Extraction from Diagram Raster Images in PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON COMPUTER RECOGNITION SYSTEMS CORES 2013
  • 2017. A Comparison of Approaches for Automated Text Extraction from Scholarly Figures in MULTIMEDIA MODELING
  • 2015-06. Exploiting colour information for better scene text detection and recognition in INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION (IJDAR)
  • 2006. Semi-automatic Ground Truth Generation for Chart Image Recognition in DOCUMENT ANALYSIS SYSTEMS VII
  • 2005. Adaptive Fuzzy Text Segmentation in Images with Complex Backgrounds Using Color and Texture in COMPUTER ANALYSIS OF IMAGES AND PATTERNS
  • 2005. Getting Computers to See Information Graphics So Users Do Not Have to in FOUNDATIONS OF INTELLIGENT SYSTEMS
  • 2015. A Robust Video Text Extraction and Recognition Approach Using OCR Feedback Information in ADVANCES IN MULTIMEDIA INFORMATION PROCESSING -- PCM 2015
  • 2015-01. Recognizing text in raster maps in GEOINFORMATICA
  • 2013-03. A general approach for extracting road vector data from raster maps in INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION (IJDAR)
  • 2009-07. Automated analysis of images in documents for intelligent document search in INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION (IJDAR)
  • 2015-06. Scene text extraction based on edges and support vector regression in INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION (IJDAR)
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11042-018-6162-7

    DOI

    http://dx.doi.org/10.1007/s11042-018-6162-7

    DIMENSIONS

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