论文标题

使用离线手写单块字符的作者识别

Writer Recognition Using Off-line Handwritten Single Block Characters

论文作者

Hagström, Adrian Leo, Stanikzai, Rustam, Bigun, Josef, Alonso-Fernandez, Fernando

论文摘要

用于各种目的的纸质表格时,通常会使用块字符。我们调查手写文本的单个数字中是否包含生物识别信息。特别是,我们使用的个人身份号码由DOB的出生日期的六位数组成。我们评估了两种识别方法,一种基于手工制作的功能,这些功能计算轮廓方向测量值,另一种基于Resnet50模型的深度特征。我们总共使用了317个人和4920个书面DOB的自捕获数据库。结果表明,与DOB相关的手写信息中,与身份相关的信息的存在。我们还分析了入学样本数量的影响,其数量在一到十之间。具有少量数据的结果是有希望的。有十个注册样本,具有深度特征的前1位准确性约为94%,近10个占100%。验证准确性更为适中,EER> 20%,具有任何给定的功能和入学设置尺寸,表明仍然有改进的余地。

Block characters are often used when filling paper forms for a variety of purposes. We investigate if there is biometric information contained within individual digits of handwritten text. In particular, we use personal identity numbers consisting of the six digits of the date of birth, DoB. We evaluate two recognition approaches, one based on handcrafted features that compute contour directional measurements, and another based on deep features from a ResNet50 model. We use a self-captured database of 317 individuals and 4920 written DoBs in total. Results show the presence of identity-related information in a piece of handwritten information as small as six digits with the DoB. We also analyze the impact of the amount of enrolment samples, varying its number between one and ten. Results with such small amount of data are promising. With ten enrolment samples, the Top-1 accuracy with deep features is around 94%, and reaches nearly 100% by Top-10. The verification accuracy is more modest, with EER>20%with any given feature and enrolment set size, showing that there is still room for improvement.

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