Classification of Elementary Stamp Shapes by Means of Reduced Point Distance Histogram Representation
The paper presents a problem of stamp shape classification, where an input stamp is given as a bitmap containing binary values. While every stamp features a specific geometrical form coming from the de facto standards of stamping process, thus it can be classified as round, oval, square, rectangular or triangular. We assume to have a detected stamp and in this paper we deal with the stage of features extraction and reduction, by means of Point Distance Histogram (at the stage of features extraction) and Principal Component Analysis and Linear Discriminant Analysis (at the stage of dimensionality reduction). The final classification employs similarity evaluation involving hand-drawn templates, ideal shapes and average descriptors calculated for the entire database. Despite the fact that there are only several basic stamp shapes, the task is not trivial since there are many variations in size, silhouette and complexity of individual stamps. It should be emphasized that the scanned document may be degraded in quality thus extracted stamp can be distorted (the silhouette may be discontinuous and/or can be noised). The paper provides some experimental results on real documents with different types of stamps and a comparison with a classical Discrete Cosine Transform (DCT) and PCA applied on image matrix.
KeywordsDimensionality Reduction Linear Discriminant Analysis Discrete Cosine Transform Recognition Rate Average Descriptor
Unable to display preview. Download preview PDF.
- 2.Ueda, K., Nakamura, Y.: Automatic verification of seal impression patterns. In: Proc. 7th. Int. Conf. on Pattern Recognition, pp. 1019–1021 (1984)Google Scholar
- 4.Zhu, G., Jaeger, S., Doermann, D.: A robust stamp detection framework on degraded documents. In: Proceedings — SPIE The International Society For Optical Engineering, vol. 6067 (2006)Google Scholar
- 5.Zhu, G., Doermann, D.: Automatic Document Logo Detection. In: The 9th International Conference on Document Analysis and Recognition (ICDAR 2007), pp. 864–868 (2007)Google Scholar
- 10.Jolliffe, I.T.: Principal Component Analysis. Springer, NY (1986)Google Scholar
- 11.Kukharev, G., Forczmański, P.: Data Dimensionality Reduction for Face Recognition. Machine Graphics & Vision 13(1/2), 99–122 (2004)Google Scholar