Details

Autor(en) / Beteiligte

• Le, Viet Phuong
• Nayef, Nibal
• Visani, Muriel
• Ogier, Jean-Marc
• Tran, Cao De

Titel

Text and non-text segmentation based on connected component features

Ist Teil von

• 2015 13th International Conference on Document Analysis and Recognition (ICDAR), 2015, p.1096-1100

Ort / Verlag

IEEE

Erscheinungsjahr

2015

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• Document image segmentation is crucial to OCR and other digitization processes. In this paper, we present a learning-based approach for text and non-text separation in document images. The training features are extracted at the level of connected components, a mid-level between the slow noise-sensitive pixel level, and the segmentation-dependent zone level. Given all types, shapes and sizes of connected components, we extract a powerful set of features based on size, shape, stroke width and position of each connected component. Adaboosting with Decision trees is used for labeling connected components. Finally, the classification of connected components into text and non-text is corrected based on classification probabilities and size as well as stroke width analysis of the nearest neighbors of a connected component. The performance of our approach has been evaluated on the two standard datasets: UW-III and ICDAR-2009 competition for document layout analysis. Our results demonstrate that the proposed approach achieves competitive performance for segmenting text and non-text in document images of variable content and degradation.