The existing handwritten numerical string recognition algorithm based on over-segmentation is highly complex, and the existing unsegmented recognition algorithm cannot recognize character strings of four digits or more and has a low accuracy rate and the low accuracy. To address these issues, an unsegmented recognition algorithm for handwritten numerical strings based on mask region convolution neural network (Mask-RCNN) is proposed. Because of Mask-RCNN adds parallel full-convolution split-segmentation subnets, it can simultaneously achieve mask segmentation of single digit in the sticky handwritten numerical string and classify digit categories. Results of the test set indicate that after the training of 1-6 numerical strings of images in NIST SD19 dataset and self-built mask-training dataset, the recognition accuracy of the network for character strings of 3 digits, 4 digits and 5 digits is 1.2 percentage, 0.6 percentage and 0.4 percentage higher, respectively, compared with the latest algorithms. The proposed algorithm exhibits significant advantages in recognizing handwritten numerical strings with unrestricted digits and has broad application prospects.