Detail of Publication

Text Language	English
Authors	Utpal Garain, Onkar Pandit, Olivier Augereau, Ayano Okoso, Koichi Kise
Title	Identification of Reader Specific Difficult Words by Analyzing Eye Gaze and Document Content
Journal	Proceedings of the International Conference on Document Analysis and Recognition
Number of Pages	6 pages
Publisher	IEEE
Reviewed or not	Reviewed
Month & Year	November 2017
Abstract	This paper presents an approach for identifying reader specific difficult words while someone is reading a textual document. The work is motivated by the need of developing human-document interaction systems, in general and creating person-specific online educational content, in particular. Eye gaze information gives person specific behavior whereas textual content is analyzed to get general linguistic aspect of the document content. These two pieces of information are fused together through machine learning algorithms to identify the set of difficult words for a particular reader reading a particular document. An annotated dataset has been created where each word in a document is marked with its bounding box information and each reader identifies a set of difficult words while reading the document. The dataset consists of sixteen documents and each document is read by five subjects. The method is evaluated through recall-precision analysis. The impressive precision at high recall attests the feasibility of building a practical application based on this research. The experiment further brings out several interesting facts about human reading behavior.

• Entry for BibTeX

  @InProceedings{Garain2017,
    author =	{Utpal Garain and Onkar Pandit and Olivier Augereau and Ayano Okoso and Koichi Kise},
    title =	{Identification of Reader Specific Difficult Words by Analyzing Eye Gaze and Document Content},
    booktitle =	{Proceedings of the International Conference on Document Analysis and Recognition},
    year =	2017,
    month =	nov,
    numpages =	{6},
    publisher =	{IEEE}
  }
  
  
  

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