Abstract: Due to many interference factors when photographing the bank card, such as the
uncertainty of shooting angle, the complexity of lighting conditions and the diversity of bank card
background, there are great challenges for the bank card digital recognition algorithm based on
natural shooting scene. Therefore, a framework for bank card recognition is proposed based on
convolution neural network (CNN). Firstly, the digital region of target bank card is obtained by
performing a series of image processing algorithms, such as projection correction, edge detection, and
morphology operation. Secondly, a convolution neural network is trained through the augmented
dataset to obtain the above target digital area for sliding window recognition. Then the initial bank
card number sequence is output to generate a digital graph. Finally, a smoothing optimization
algorithm is proposed, which inputs the above initial bank card number graph and optimizes it. Then
the digital sequence is divided into individual numbers and the final result is output. The experimental
results show that the algorithm significantly improves the accuracy of bank card digital recognition
and segmentation. At the same time, it still has good robustness for those bank cards with more
complex images.

Key words: bank card recognize, convolution neural network, digital recognition, digital segmentation;
smooth algorithm