Recovery of temporal information from static images of handwriting
International Journal of Computer Vision - Special issue: image understanding research at the University of Maryland
Computer graphics (2nd ed.): C version
Computer graphics (2nd ed.): C version
A Full English Sentence Database for Off-Line Handwriting Recognition
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
A set of handwriting families: style recognition
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Forgery detection by local correspondence
Forgery detection by local correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Writer Identification Using Fragmented Connected-Component Contours
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
A writer identification and verification system
Pattern Recognition Letters
Off-line Chinese signature verification based on support vector machines
Pattern Recognition Letters
A writer identification and verification system using HMM based recognizers
Pattern Analysis & Applications
Text-Independent Writer Identification and Verification Using Textural and Allographic Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Handwriting Identification on Medieval Documents
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Off-Line Multi-Script Writer Identification Using AR Coefficients
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Offline text-independent writer identification using codebook and efficient code extraction methods
Image and Vision Computing
An optimization for binarization methods by removing binary artifacts
Pattern Recognition Letters
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As suggested by modern paleography, the width of ink traces is a powerful source of information for off-line writer identification, particularly if combined with its direction. Such measurements can be computed using simple, fast and accurate methods based on pixel contours, the combination of which forms a powerful feature for writer identification: the Quill feature. It is a probability distribution of the relation between the ink direction and the ink width. It was tested in writer identification experiments on two datasets of challenging medieval handwriting and two datasets of modern handwriting. The feature achieved a nearest-neighbor accuracy in the range of 63-95%, which even approaches the performance of two state-of-the-art features in contemporary-writer identification (Hinge and Fraglets). The feature is intuitive and explainable and its principle is supported by a model of trace production by a quill. It illustrates that ink width patterns are valuable. A slightly more complex variant of Quill, QuillHinge, scored 70-97% writer identification accuracy. The features are already being used by domain experts using a graphical interface.