ImprovingWriter Identification by Means of Feature Selection and Extraction
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A writer identification system for on-line whiteboard data
Pattern Recognition
Invariants Discretization for Individuality Representation in Handwritten Authorship
IWCF '08 Proceedings of the 2nd international workshop on Computational Forensics
Offline handwritten character recognition of Gujrati script using pattern matching
ASID'09 Proceedings of the 3rd international conference on Anti-Counterfeiting, security, and identification in communication
Embedded scale united moment invariant for identification of handwriting individuality
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
Writer identification using fractal dimension of wavelet subbands in gabor domain
Integrated Computer-Aided Engineering
Evaluation of biometric identification in open systems
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Handwriting recognition accuracy improvement by author identification
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Writer identification for smart meeting room systems
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Identifying the writer of ancient inscriptions and Byzantine codices. A novel approach
Computer Vision and Image Understanding
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In this paper, an off-line, text independent system for writer identification using Hidden Markov Model (HMM) based recognizers is described. For each writer we build an individual recognizer and train it on text lines written by that writer. A text line of unknown origin is presented to each of these recognizers. As a result we get, from each recognizer, a transcription including the log-likelihood score for the considered input. We rank all scores, and based on the assumption that the recognizer with the highest log-likelihood is the one that has been trained using text lines of this writer, we assign the text line to the writer whose score ranks first. We tested our system using over 2,200 text lines from 50 writers and have in 94.47% of all cases correctly identified the writer. Using a simple confidence measure to define a rejection mechanism, we achieved an error rate of 0% by rejecting 15% of the results.