Handwritten biometric recognition is the process of identifying the author of a given text from the handwriting style. Handwritten biometric recognition belongs to behavioural biometric systems because it is based on something that the user has learned to do.
![](http://upload.wikimedia.org/wikipedia/commons/thumb/a/a8/Numeros_dinamicos.jpg/450px-Numeros_dinamicos.jpg)
![](http://upload.wikimedia.org/wikipedia/commons/thumb/4/4c/Info_numeros.jpg/450px-Info_numeros.jpg)
Static and dynamic recognition
editHandwritten biometrics can be split into two main categories:
Static: In this mode, users writes on paper, digitize it through an optical scanner or a camera, and the biometric system recognizes the text analyzing its shape. This group is also known as "off-line".
Dynamic: In this mode, users writes in a digitizing tablet, which acquires the text in real time. Another possibility is the acquisition by means of stylus-operated PDAs. Dynamic recognition is also known as "on-line". Dynamic information for handwriting movement analysis usually consists of the following information:
- spatial coordinate x(t)
- spatial coordinate y(t)
- pressure p(t)
- azimuth az(t)
- inclination in(t)
Better accuracies are achieved by means of dynamic systems. Some technological approaches exist.[1][2][3][4][5]
Difference from OCR
editHandwritten biometric recognition should not be confused with optical character recognition (OCR). While the goal of handwritten biometrics is to identify the author of a given text, the goal of an OCR is to recognize the content of the text, regardless of its author.
References
edit- ^ Chapran, J. (2006). "Biometric Writer Identification: Feature Analysis and Classification". International Journal of Pattern Recognition & Artificial Intelligence. 20 (4): 483–503. doi:10.1142/S0218001406004831.
- ^ Schomaker, L. (2007). "Advances in Writer Identification and Verification". Ninth International Conference on Document Analysis and Recognition. ICDAR: 1268–1273. Archived from the original on 2021-01-28. Retrieved 2020-10-12.
- ^ Said, H. E. S.; TN Tan; KD Baker (2000). "Personal identification based on handwriting". Pattern Recognition. 33 (2000): 149–160. Bibcode:2000PatRe..33..149S. CiteSeerX 10.1.1.408.9131. doi:10.1016/S0031-3203(99)00006-0.
- ^ Schlapbach, A.; M Liwicki; H Bunke (2008). "A writer identification system for on-line whiteboard data". Pattern Recognition. 41 (7): 2381–2397. Bibcode:2008PatRe..41.2381S. doi:10.1016/j.patcog.2008.01.006.
- ^ Sesa-Nogueras, Enric; Marcos Faundez-Zanuy (2012). "Biometric recognition using online uppercase handwritten text". Pattern Recognition. 45 (1): 128–144. Bibcode:2012PatRe..45..128S. doi:10.1016/j.patcog.2011.06.002.