基于Mask-RCNN无分割手写数字字符串的识别

Unsegmented Recognition of Handwritten Numerical Strings Based on Mask-RCNN

针对现有基于过分割的手写体数字字符串识别算法的复杂度较高,以及基于无分割算法无法识别4位及以上长度字符串和准确率较低的问题,提出了基于掩模区域神经网络(Mask-RCNN)的无分割手写数字字符串的识别算法。由于Mask-RCNN增加了并行的全卷积分割子网,能够同时实现对粘连手写数字串中单个数字的掩模分割和数字类别的分类任务。测试集的结果表明,在NIST SD19数据集的1~6位数字串图像及自建掩模数据集的训练下,该网络对长度分别为3位,4位和5位字符串的识别准确率比目前最新算法分别提高了1.2、0.6、0.4个百分点,该算法对非限制位数的手写体数字串的识别具有显著优势,应用前景广阔。

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.