Signature segmentation and recognition from scanned documents
- Publication Type:
- Conference Proceeding
- International Conference on Intelligent Systems Design and Applications, ISDA, 2014, pp. 80 - 85
- Issue Date:
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© 2013 IEEE. Signature as a query is important for content-based document image retrieval from a scanned document repository. This paper presents a two-stage approach towards automatic signature segmentation and recognition from scanned document images. In the first stage, signature blocks are segmented from the document using word-wise component extraction and classification. Gradient based features are extracted from each component at the word level to perform the classification task. In the 2nd stage, SIFT (Scale-Invariant Feature Transform) descriptors and Spatial Pyramid Matching (SPM)-based approaches are used for signature recognition. Support Vector Machines (SVMs) are employed as the classifier for both levels in this experiment. The experiments are performed on the publicly available 'Tobacco-800' and GPDS  datasets and the results obtained from the experiments are promising.
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