Learning to Detect Tables in Scanned Document Images using Line Information

Thotreingam Kasar (1) , Philippine Barlas (1) , Adam Sébastien (1) , Clément Chatelain (1) , Thierry Paquet (1)

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This paper presents a method to detect table regions in document images by identifying the column and row line-separators and their properties. The method employs a run-length approach to identify the horizontal and vertical lines present in the input image. From each group of intersecting horizontal and vertical lines, a set of 26 low-level features are extracted and an SVM classifier is used to test if it belongs to a table or not. The performance of the method is evaluated on a heterogeneous corpus of French, English and Arabic documents that contain various types of table structures and compared with that of the Tesseract OCR system.

Mots clés: Table detection; line detection

Domaines: Traitement du texte et du document

Thotreingam Kasar, Philippine Barlas, Adam Sébastien, Clément Chatelain, Thierry Paquet. Learning to Detect Tables in Scanned Document Images using Line Information. Internation conférence on document analysis and recognition, Aug 2013, Washington, United States. pp.1185 - 1189, ⟨10.1109/ICDAR.2013.240⟩. ⟨hal-00934902⟩