Automatic identification of writers
Conference of the Dutch Psychonomic Society on Human-computer interaction: psychonomic aspects
A set of handwriting families: style recognition
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
ImprovingWriter Identification by Means of Feature Selection and Extraction
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Pattern Recognition Letters
Text-Independent Writer Identification and Verification Using Textural and Allographic Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
A writer identification system for on-line whiteboard data
Pattern Recognition
Writer identification using global wavelet-based features
Neurocomputing
Writer Identification of Chinese Handwriting Using Grid Microstructure Feature
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Writer identification using fractal dimension of wavelet subbands in gabor domain
Integrated Computer-Aided Engineering
Handwriting recognition accuracy improvement by author identification
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Texture-based descriptors for writer identification and verification
Expert Systems with Applications: An International Journal
Word level script recognition for Uighur document mixed with English script
Proceedings of the 4th International Workshop on Multilingual OCR
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This paper evaluates the performance of edge-based directionalprobability distributions as features in writer identificationin comparison to a number of non-angular features.It is noted that the joint probability distribution of theangle combination of two "hinged" edge fragments outperformsall other individual features. Combining features mayimprove the performance. Limitations of the method pertainto the amount of handwritten material needed in orderto obtain reliable distribution estimates. The global featurestreated in this study are sensitive to major style variation(upper- vs lower case), slant, and forged styles, whichnecessitates the use of other features in realistic forensicwriter identification procedures.