A new framework for recognition of heavily degraded characters in historical typewritten documents based on semi-supervised clustering

Pletschacher, S; Hu, J; Antonacopoulos, A

Abstract

This paper presents a new semi-supervised clustering
framework to the recognition of heavily degraded characters
in historical typewritten documents, where off-theshelf
OCR typically fails. The constraints are generated
using typographical (collection-independent) domain
knowledge and are used to guide both sample (glyph set)
partitioning and metric learning. Experimental results using
simple features provide encouraging evidence that
this approach can lead to significantly improved clustering
results compared to simple K-Means clustering, as
well as to clustering using a state-of-the art OCR engine.

Citation

Pletschacher, S., Hu, J., & Antonacopoulos, A. (2009). A new framework for recognition of heavily degraded characters in historical typewritten documents based on semi-supervised clustering. In 2009 10th International Conference on Document Analysis and Recognition. https://doi.org/10.1109/ICDAR.2009.267

• Conference Name: 10th International Conference on Document Analysis and Recognition
• Conference Location: Barcelona
• Start Date: Jul 26, 2009
• End Date: Jul 29, 2009
• Publication Date: Jan 1, 2009
• Deposit Date: Dec 21, 2011
• Book Title: 2009 10th International Conference on Document Analysis and Recognition
• ISBN: 9781424445004
• DOI: https://doi.org/10.1109/ICDAR.2009.267
• Publisher URL: http://dx.doi.org/10.1109/ICDAR.2009.267