On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Pretopological Approach for Image Segmentation and Edge Detection
Journal of Mathematical Imaging and Vision
Offline signature verification using the discrete radon transform and a hidden Markov model
EURASIP Journal on Applied Signal Processing
A novel technique for palmprint classification and authentication
International Journal of Biometrics
Off-line signature verification and forgery detection using fuzzy modeling
Pattern Recognition
| Hi-index | 0.01 |
Extracting a signature from a check with a patterned background is a thorny problem in image segmentation. Methods based on threshold techniques often necessitate meticulous postprocessing in order to correctly capture the handwritten information. In this study, we tackle the problem of extracting handwritten information by means of an intuitive approach that is close to human visual perception, defining a topological criterion specific to handwritten lines which we call filiformity. This approach was inspired by the existence in the human eye of cells whose specialized task is the extraction of lines. First, we define two topological measures of filiformity for binary objects. Next, we extend these measures to include gray-level images. One of these measures, which is particularly interesting, differentiates the contour lines of objects from the handwritten lines we are trying to isolate. The local value provided by this measure is then processed by global thresholding, taking into account information about the whole image. This processing step ends with a simple fast algorithm. Evaluation of the extraction algorithm carried out on 540 checks with 16 different background patterns demonstrates the robustness of the algorithm, particularly when the background depicts a scene