This paper presents an innovative approach to student identification during exams and knowledge tests, which overcomes the limitations of the traditional personal information entry method. The proposed method employs a matrix template on the designated section of the exam, where squares containing numbers are selectively blackened. The methodology involves the development of a neural network specifically designed for recognizing students' personal identification numbers. The neural network utilizes a specially adapted U-Net architecture, trained on an extensive dataset comprising images of blackened tables. The network demonstrates proficiency in recognizing the patterns and arrangement of blackened squares, accurately interpreting the information inscribed within them. Additionally, the model exhibits high accuracy in correctly identifying entered student personal numbers and effectively detecting erroneous entries within the table. This approach offers multiple advantages. Firstly, it significantly accelerates the exam marking process by automatically extracting identifying information from the blackened tables, eliminating the need for manual entry and minimizing the potential for errors. Secondly, the method automates the identification process, thereby reducing administrative effort and expediting data processing. The introduction of this innovative identification system represents a notable advancement in the field of exams and knowledge tests, replacing the conventional manual entry of personal data with a streamlined, efficient, and accurate identification process.

翻译：本文提出了一种创新的考试与知识测试中学生身份识别方法，克服了传统个人信息录入方式的局限性。该方法在试卷指定区域采用矩阵模板，其中包含数字的方格被选择性涂黑。核心技术路线包括开发一种专用于识别学生个人学号的神经网络。该神经网络采用经过特别适配的U-Net架构，使用包含大量涂黑表格图像的数据集进行训练。网络能够精准识别涂黑方格的图案与排列方式，准确解读其承载的数字信息。此外，模型在正确识别已输入的学生学号方面表现出高准确性，并能有效检测表格中的错误录入。该方法具有多重优势：首先，通过自动提取涂黑表格中的身份信息，显著加速了阅卷流程，消除了人工录入需求并最大限度降低错误风险；其次，该自动化识别过程减少了行政工作量并加快了数据处理速度。这种创新识别系统的引入标志着考试与知识测试领域的重大进步，用精简、高效且准确的识别流程取代了传统的手工个人信息录入方式。