Abstract: Disclosed is a banknote accumulating device, including a storage chamber and a limiting mechanism arranged above the storage chamber; the storage chamber includes an inlet for inputting banknotes; the limiting mechanism includes a reset member, a driving mechanism and multiple limiting members which can move independently of each other; the reset member is configured to enable each of limiting members to have a tendency to move to a first position; the driving mechanism is configured to selectively drive one of the multiple limiting members to move from a first position to a second position, so that the limiting part of the limiting member reaches in the moving path of the banknotes in the storage chamber; the distances between the positions of the limiting parts of multiple limiting members in the moving path and the inlet are different. Further disclosed are a limiting mechanism and a cash recycling and handling device.

Abstract: The present invention discloses a method for recognizing a handwritten character, which includes the following steps of: obtaining a coarse classification template and a fine classification template; receiving a handwritten character input signal from a user, gathering a discrete coordinate sequence of trajectory points of the inputted character, and pre-processing the discrete coordinate sequence; extracting eigenvalues and calculating a multi-dimensional eigenvector of the inputted character; matching the inputted character with the coarse classification template to select a plurality of the most similar candidate character classes; and matching the eigen-transformed inputted character with sample centers of the candidate character classes selected from the fine classification template, and determining the most similar character classes among the candidate character classes. The present invention further discloses a system for recognizing a handwritten character.

Abstract: The present invention discloses a method for recognizing a handwritten character, which includes the following steps of: obtaining a coarse classification template and a fine classification template; receiving a handwritten character input signal from a user, gathering a discrete coordinate sequence of trajectory points of the inputted character, and pre-processing the discrete coordinate sequence; extracting eigenvalues and calculating a multi-dimensional eigenvector of the inputted character; matching the inputted character with the coarse classification template to select a plurality of the most similar candidate character classes; and matching the eigen-transformed inputted character with sample centers of the candidate character classes selected from the fine classification template, and determining the most similar character classes among the candidate character classes. The present invention further discloses a system for recognizing a handwritten character.