Text/Graphics Segmentation in Architectural Floor Plans

Text/Graphics Segmentation in Architectural Floor Plans
Sheraz Ahmed, Markus Weber, Marcus Liwicki, Andreas Dengel
International Conference on Document Analysis and Recognition International Conference on Document Analysis and Recognition (ICDAR-2011), 11th, September 18-21, Beijing, China

Abstract:: In this paper, we propose an improved method for text/graphics segmentation. Text/graphics separation is a crucial preprocessing step in document analysis before further analysis and recognition can be applied. Our proposed system extends the method of Tombre et al. with a number of improvements to make it more suitable for architectural floor plans. A crucial novel preprocessing step is the detection and removal of walls before the actual segmentation. Furthermore, text components are then extracted by analyzing connected components and even considering text overlapping with graphics. Finally, a smearing approach is used to remove noise and extract the final text components. Evaluation results over the series of 90 floor plans which has also been used in reference work shows that our method has a recall of almost 99% and a precision greater then 97 %.

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Text/Graphics Segmentation in Architectural Floor Plans

Abstract:: In this paper, we propose an improved method for text/graphics segmentation. Text/graphics separation is a crucial preprocessing step in document analysis before further analysis and recognition can be applied. Our proposed system extends the method of Tombre et al. with a number of improvements to make it more suitable for architectural floor plans. A crucial novel preprocessing step is the detection and removal of walls before the actual segmentation. Furthermore, text components are then extracted by analyzing connected components and even considering text overlapping with graphics. Finally, a smearing approach is used to remove noise and extract the final text components. Evaluation results over the series of 90 floor plans which has also been used in reference work shows that our method has a recall of almost 99% and a precision greater then 97 %.

Back to the overview

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