Automatic document orientation detection and categorization through document vectorization

ABSTRACT

This paper presents an automatic orientation detection and categorization technique that is capable of detecting the orientation of multilingual documents with arbitrary skew and categorizing document images according to the underlying languages. We carry out orientation detection and categorization through document vectorization, which encodes document orientation and language information and converts each document image into an electronic document vector through the exploitation of the density and distribution of vertical component runs. For each language of interest, a pair of vector templates is first constructed through a training process. Orientation and category of the query image are then determined based on distances between the query document vector and the constructed vector templates. Experiments over 492 testing document images show that the average orientation detection and categorization rates reach up to 97.56% and 99.59%, respectively.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.10 Vision and Scene Understanding
            Subjects: Shape

Additional Classification:
  I. Computing Methodologies
  I.7 DOCUMENT AND TEXT PROCESSING
      I.7.5 Document Capture
          Subjects: Document analysis; Scanning

General Terms:
Algorithms, Experimentation

Keywords:
document image, document orientation detection