Local Orientation Extraction for Wordspotting in Syriac Manuscripts
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Indexation of Syriac manuscripts using directional features
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Rotation-invariant texture features from the steered Hermite transform
Pattern Recognition Letters
Texture feature evaluation for segmentation of historical document images
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
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In this paper, we propose a biologically inspired, global and segmentation free methodology for manuscript noise reduction and classification. Our method consists of developing well-adapted tools for writing enhancement, background noise, text and drawing separation and handwritten patterns characterization with orientation features. We have used here analysis of handwritten images in the spectral domain by frequency decompositions (Hermite transforms) and Gabor filtering for selective text information extraction. We have tested our approach of writing classification on ancient manuscripts corpus, mainly composed of 18th century authors’ documents. The current results are very promising: they show that our biologically inspired methodology can be efficiently used for handwriting analysis without any a priori grapheme segmentation.