Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Bibliography references validation using emergent architecture
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Quality enhancement in information extraction from scanned documents
Proceedings of the 2006 ACM symposium on Document engineering
Using web resources for support of online-browsing of research papers
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
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Authors' names are a critical bibliographic element when searching or browsing academic articles stored in digital libraries. Therefore, those creating metadata for digital libraries would appreciate an automatic method to extract such bibliographic data from printed documents. In this paper, we describe an automatic author name tagger for academic articles scanned with optical character recognition (OCR) mark-up. The method uses conditional random fields (CRF) for labeling the unsegmented character strings in authors' blocks as those of either an author or a delimiter. We applied the tagger to Japanese academic articles. The results of the experiments showed that it correctly labeled more than 99% of the author name strings, which compares favorably with the under 96% correct rate of our previous tagger based on a hidden Markov model (HMM).