Segmentation of the Date in Entries of Historical Church Registers
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Mathematical Morphology and Weighted Least Squares to Correct Handwriting Baseline Skew
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Baseline Estimation For Arabic Handwritten Words
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Line Detection and Segmentation in Historical Church Registers
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Accuracy Improvement of Slant Estimation for Handwritten Words
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Cursive word skew/slant corrections based on Radon transform
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Online slant identification algorithm for curved strokes
SEPADS'08 Proceedings of the 7th WSEAS International Conference on Software Engineering, Parallel and Distributed Systems
Slant Classification Using FuzzySIS
ICCIT '08 Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology - Volume 01
Baseline detection for on-line cursive Thai handwriting recognition based on PCA
KICSS'10 Proceedings of the 5th international conference on Knowledge, information, and creativity support systems
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This paper covers the area of baseline identification, which leads to signature recognition. It addresses the usage of a proposed algorithm, which identifies the minima points of a signature to be applied in signature baseline recognition. Signature baseline is vaguely identifiable and hard to determine for its baseline form. In this study, the aim is to determine the baseline form and categorizing it into ascending, descending and normal baseline. An algorithm using local minima selection technique is proposed in solving this problem. The total of 100 acquired signatures is used to determine the baseline classification range. Identifiable minima point values are extracted using an identification algorithm to yield a distribution of data that would represent the signature baseline. Then, a linear regression formula is applied to identify the direction of the baseline. The result is then tested for its accuracy with an available 100 sample of expert verified signatures. The result shows a favorable accuracy of 76% correct baseline identification. It is hoped that the implementation of this technique would be able to give some degree of contribution in the area of signature or handwriting baseline recognition.