Off-Line Cursive Script Word Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Automatic Detection of Handwriting Forgery
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
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
Text-Independent Writer Identification and Verification Using Textural and Allographic Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-Uniform Slant Correction for Handwritten Text Line Recognition
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Towards Explainable Writer Verification and Identification Using Vantage Writers
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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Slant is a salient feature of Western handwriting and it is considered to be an important writer-specific feature. In disguised handwriting however, slant is often modified. It was tested whether slant is indeed an important factor and it was tested whether the distorting effect of deliberate slant change can be countered by a simple shear transform. This was done in two off-line writer verification experiments in image processing conditions of slant elimination and slant correction. The experiments were performed using three features based on statistical pattern recognition, including the state-of-the-art features Fraglets and Hinge. A new public dataset was created and used, containing natural and slanted handwriting by 47 writers. A striking result is that the average natural slant value is much less important for biometric systems than is usually assumed: eliminating slant yields just a 1-5% performance loss. A second result is that the effects of deliberate slant change cannot be fully countered by a simple shear transform: it raises performance on the distorted handwriting from 53-68% to 64-90%, but this is still lower than normal operation on natural handwriting: 97-100%.