A supervised algorithm with a new differentiated-weighting scheme for identifying the author of a handwritten text

Authors:
Edith C. Herrera-Luna;Edgardo M. Felipe-Riveron;Salvador Godoy-Calderon
Affiliations:
Artificial Intelligence Laboratory, Center for Computing Research, National Polytechnic Institute, Juan de Dios Batiz and Miguel Othon de Mendizabal, P.O. 07738, Gustavo A Madero, Mexico;Artificial Intelligence Laboratory, Center for Computing Research, National Polytechnic Institute, Juan de Dios Batiz and Miguel Othon de Mendizabal, P.O. 07738, Gustavo A Madero, Mexico;Artificial Intelligence Laboratory, Center for Computing Research, National Polytechnic Institute, Juan de Dios Batiz and Miguel Othon de Mendizabal, P.O. 07738, Gustavo A Madero, Mexico
Venue:
Pattern Recognition Letters
Year:
2011

Citing 7
Cited 0

Recognition of handwritten and machine-printed text for postal address interpretation

Pattern Recognition Letters - Postal processing and character recognition
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey

IEEE Transactions on Pattern Analysis and Machine Intelligence
Writer Identification Using Text Line Based Features

ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
A writer identification and verification system

Pattern Recognition Letters
Extraction and analysis of forensic document examiner features used for writer identification

Pattern Recognition
A set of novel features for writer identification

AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Nearest neighbor pattern classification

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.10

Visualization

Abstract

In this paper a new approach is presented for tackling the problem of identifying the author of a handwritten text. This problem is solved with a simple, yet powerful, modification of the so called ALVOT family of supervised classification algorithms with a novel differentiated-weighting scheme. Compared to other previously published approaches, the proposed method significantly reduces the number and complexity of the text-features to be extracted from the text. Also, the specific combination of line-level and word-level features used introduces an eclectic paradigm between texture-related and structure-related approaches.