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This paper presents an innovative approach to student identification duringexams and knowledge tests, which overcomes the limitations of the traditionalpersonal information entry method. The proposed method employs a matrixtemplate on the designated section of the exam, where squares containingnumbers are selectively blackened. The methodology involves the development ofa neural network specifically designed for recognizing students' personalidentification numbers. The neural network utilizes a specially adapted U-Netarchitecture, trained on an extensive dataset comprising images of blackenedtables. The network demonstrates proficiency in recognizing the patterns andarrangement of blackened squares, accurately interpreting the informationinscribed within them. Additionally, the model exhibits high accuracy incorrectly identifying entered student personal numbers and effectivelydetecting erroneous entries within the table. This approach offers multipleadvantages. Firstly, it significantly accelerates the exam marking process byautomatically 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 reducingadministrative effort and expediting data processing. The introduction of thisinnovative identification system represents a notable advancement in the fieldof exams and knowledge tests, replacing the conventional manual entry ofpersonal data with a streamlined, efficient, and accurate identificationprocess.

Recognizing student identification numbers from the matrix templates using a modified U-net architecture