Next-generation Optical Mark Recognition powered by OpenCV algorithms, AI question synthesis, and enterprise-grade 2FA protection designed for schools and universities.
Live statistical evaluation aggregated from 2,063 real optical answer sheets and 39,135 verified bubble answers.
Student performance classifications across all exam papers
Monthly volume of OMR answer sheets processed
Graded test volume by questionnaire curriculum
Analysis of 39,135 bubble selections
Every element is tuned for rapid paper processing, anti-cheat validation, and institutional compliance.
Create answer sheets with QR codes and full Cyrillic support
Instant processing of scanned answer sheets using OpenCV
Generate questions with GPT-4o-mini in seconds
Mandatory two-factor authentication for maximum security
Create multiple exam variants to prevent cheating
Complete results export with advanced filtering
Full support for Russian, Uzbek, and other languages
Precise detection tuning with real-time preview
From secondary school classrooms to multi-department university exam halls.
Automate checking of assignments and tests
Conduct exams and midterm assessments
Test and certify course participants
Meet the specialized engineers, system architects, and education domain experts powering the OMR System platform.
OMR System is an advanced Optical Mark Recognition platform built specifically to automate and accelerate test and exam evaluation across schools, colleges, and higher educational institutions in Uzbekistan.
The platform unites computer vision algorithms with OpenAI GPT-4o-mini generation and robust Django security to guarantee 99.9% optical precision and instant result dissemination.
Under 3 seconds per scanned sheet.
Perspective transformation and adaptive thresholding.
2FA TOTP authentication and strict session tokens.
Native support for Uzbek, Russian, and English.
The OMR System platform has successfully passed all testing, stress-testing, and examination grading validation trials at the Cyber University of Uzbekistan, confirming 99.9% optical recognition accuracy and rock-solid reliability under actual academic conditions.
Fully compliant with International Data Protection standards and recommendations from the Ministry of Higher Education, Science and Innovation of the Republic of Uzbekistan.