@inproceedings{3de7c61f57ac4b53aa7df50f7f59c829,
title = "Offline writer identification using K-adjacent segments",
abstract = "This paper presents a method for performing offline writer identification by using K-adjacent segment (KAS) features in a bag-of-features framework to model a user's handwriting. This approach achieves a top 1 recognition rate of 93\% on the benchmark IAM English handwriting dataset, which outperforms current state of the art features. Results further demonstrate that identification performance improves as the number of training samples increase, and additionally, that the performance of the KAS features extend to Arabic handwriting found in the MADCAT dataset.",
keywords = "Codebook, Document Forensics, Handwriting, K-Adjacent Segments, Local Features, Writer Identification",
author = "Rajiv Jain and David Doermann",
year = "2011",
doi = "10.1109/ICDAR.2011.159",
language = "English",
isbn = "9780769545202",
series = "Proceedings of the International Conference on Document Analysis and Recognition, ICDAR",
pages = "769--773",
booktitle = "Proceedings - 11th International Conference on Document Analysis and Recognition, ICDAR 2011",
note = "11th International Conference on Document Analysis and Recognition, ICDAR 2011 ; Conference date: 18-09-2011 Through 21-09-2011",
}