Abstract: There are many defects in the mobility and convenience of the handwritten digit recognition system running on the computer. In order to make improvement for these defects, a handwritten digit recognition system based on the visual library OpenCV is designed, which transplants the digit recognition algorithm into the flexible and small high-performance embedded equipment. By adjusting the shooting Angle of the steering gear and using the technology of picture splicing and digital segmentation, the handwritten digit recognition of short distance and large area is realized. The recognition speed, recognition accuracy and model volume of the models trained by KNN(K-Nearest Neighbor), support vector machine and artificial neural network are compared. After testing, the identification time of Raspberry Pi by using the artificial neural network algorithm can be as low as 0. 115 s, and the recognition accuracy can reach 72% , which has a certain application value.

Key words: handwritten digit recognition, artificial network, model training, OpenCV vision library