Writer Identification Using Text Line Based Features
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Individuality of Handwritten Characters
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Information Retrieval Based Writer Identification
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using HMM Based Recognizers for Writer Identification and Verification
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Automatic Writer Identification Using Fragmented Connected-Component Contours
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Handwriting Analysis for Writer Verification
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Text-Independent Writer Identification and Verification Using Textural and Allographic Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Offline text-independent writer identification using codebook and efficient code extraction methods
Image and Vision Computing
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An effective method for writer identification and veri- fication is based on assuming that each writer acts as a stochastic generator of ink-trace fragments, or graphemes. The probability distribution of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common codebook of graphemes obtained by clustering. In previous studies we used contours to encode the graphemes, in the current paper we explore a complementary shape representation using normalized bitmaps. The most important aim of the current work is to compare three different clustering methods for generating the grapheme codebook: k-means, Kohonen SOM 1D and 2D. Large scale computational experiments show that the proposed method is robust to the underlying shape representation used (whether contours or normalized bitmaps), to the size of codebook used (stable performance for sizes from 102 to 2.5脳103) and to the clustering method used to generate the codebook (essentially the same performance was obtained for all three clustering methods).