Automatic detection of pseudocodes in scholarly documents using machine learning

Suppawong Tuarob, Sumit Bhatia, Prasenjit Mitra, C. Lee Giles

Research output: Contribution to journalConference article

27 Citations (Scopus)


A significant number of scholarly articles in computer science and other disciplines contain algorithms that provide concise descriptions for solving a wide variety of computational problems. For example, Dijkstra's algorithm describes how to find the shortest paths between two nodes in a graph. Automatic identification and extraction of these algorithms from scholarly digital documents would enable automatic algorithm indexing, searching, analysis and discovery. An algorithm search engine, which identifies pseudocodes in scholarly documents and makes them searchable, has been implemented as a part of the CiteSeerX suite. Here, we illustrate the limitations of start-of-the-art rule based pseudocode detection approach, and present a novel set of machine learning based techniques that extend previous methods.

Original languageEnglish
Article number6628716
Pages (from-to)738-742
Number of pages5
JournalProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Publication statusPublished - 11 Dec 2013
Event12th International Conference on Document Analysis and Recognition, ICDAR 2013 - Washington, DC, United States
Duration: 25 Aug 201328 Aug 2013



  • Algorithm
  • Classification
  • Experiment
  • Machine Learning
  • Pseudocode

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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