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has been cited by the following article:

• TITLE: Automatic Table Recognition and Extraction from Heterogeneous Documents

  AUTHORS: Florence Folake Babatunde, Bolanle Adefowoke Ojokoh, Samuel Adebayo Oluwadare

  KEYWORDS: Hidden Markov Model, Table Recognition and Extraction, Hypertext Markup Language, Heterogeneous Documents

  JOURNAL NAME: Journal of Computer and Communications, Vol.3 No.12, December 30, 2015

  ABSTRACT: This paper examines automatic recognition and extraction of tables from a large collection of het-erogeneous documents. The heterogeneous documents are initially pre-processed and converted to HTML codes, after which an algorithm recognises the table portion of the documents. Hidden Markov Model (HMM) is then applied to the HTML code in order to extract the tables. The model was trained and tested with five hundred and twenty six self-generated tables (three hundred and twenty-one (321) tables for training and two hundred and five (205) tables for testing). Viterbi algorithm was implemented for the testing part. The system was evaluated in terms of accuracy, precision, recall and f-measure. The overall evaluation results show 88.8% accuracy, 96.8% precision, 91.7% recall and 88.8% F-measure revealing that the method is good at solving the problem of table extraction.