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Conference papers

Learning Texture Features for Enhancement and Segmentation of Historical Document Images

Abstract : Many challenges and open issues related to the tremendous growth in digitizing collections of cultural heritage documents have been raised, such as information retrieval in digital libraries or analyzing page content of historical books. Recently, graphic/text segmentation in historical documents has posed specific challenges due to many particularities of historical document images (e.g. noise and degradation, presence of handwriting, overlapping layouts, great variability of page layout). To cope with those challenges, a method based on learning texture features for historical document image enhancement and segmentation is proposed in this article. The proposed method is based on using the simple linear iterative clustering (SLIC) superpixels, Gabor de-scriptors and support vector machines (SVM). It has been evaluated on 100 document images which have been selected from the databases of the competitions (i.e. historical document layout analysis and historical book recognition) in the context of ICDAR conference and HIP workshop (2011 and 2013). To demonstrate the enhancement and segmentation quality, the evaluation is based on manually labeled ground truth and shows the effectiveness of the proposed method through qualitative and numerical experiments. The proposed method provides interesting results on historical document images having various page layouts and different typographical and graphical properties.
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Submitted on : Wednesday, December 2, 2015 - 10:35:25 PM
Last modification on : Thursday, May 12, 2022 - 3:39:21 PM
Long-term archiving on: : Thursday, March 3, 2016 - 3:01:31 PM


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  • HAL Id : hal-01237228, version 1


Maroua Mehri, Nibal Nayef, Pierre Héroux, Petra Gomez-Krämer, Rémy Mullot. Learning Texture Features for Enhancement and Segmentation of Historical Document Images. International Workshop on Historical Document Imaging and Processing (HIP), Aug 2015, Nancy, France. pp.47-54. ⟨hal-01237228⟩



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