Signature recognition is an example of behavioral biometrics that identifies a person based on their handwriting. It can be operated in two different ways:
Static: In this mode, users write their signature on paper, and after the writing is complete, it is digitized through an optical scanner or a camera to turn the signature image into bits. [1] The biometric system then recognizes the signature analyzing its shape. This group is also known as "off-line". [2]
Dynamic: In this mode, users write their signature in a digitizing tablet, which acquires the signature in real time. Another possibility is the acquisition by means of stylus-operated PDAs. Some systems also operate on smart-phones or tablets with a capacitive screen, where users can sign using a finger or an appropriate pen. Dynamic recognition is also known as "on-line". Dynamic information usually consists of the following information: [2]
The state-of-the-art in signature recognition can be found in the last major international competition. [3]
The most popular pattern recognition techniques applied for signature recognition are dynamic time warping, hidden Markov models and vector quantization. Combinations of different techniques also exist. [4]
Recently, a handwritten biometric approach has also been proposed. [5] In this case, the user is recognized analyzing his handwritten text (see also Handwritten biometric recognition).
Several public databases exist, being the most popular ones SVC, [6] and MCYT. [7]
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: CS1 maint: DOI inactive as of July 2025 (link)