  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.
