  This paper describes the experimental framework and results of the ICDAR 2021
Competition on On-Line Signature Verification (SVC 2021). The goal of SVC 2021
is to evaluate the limits of on-line signature verification systems on popular
scenarios (office/mobile) and writing inputs (stylus/finger) through
large-scale public databases. Three different tasks are considered in the
competition, simulating realistic scenarios as both random and skilled
forgeries are simultaneously considered on each task. The results obtained in
SVC 2021 prove the high potential of deep learning methods. In particular, the
best on-line signature verification system of SVC 2021 obtained Equal Error
Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3).
  SVC 2021 will be established as an on-going competition, where researchers
can easily benchmark their systems against the state of the art in an open
common platform using large-scale public databases such as DeepSignDB and
SVC2021_EvalDB, and standard experimental protocols.

Computer Vision and Pattern Recognition;Human-Computer Interaction