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SurveyNet: A Unified Deep Learning Framework for OCR and OMR-Based Survey Digitization.

Rubi Quiñones1, Sreeja Cheekireddy1, Eren Gultepe1

  • 1Department of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL 62026, USA.

Journal of Imaging
|April 27, 2026
PubMed
Summary
This summary is machine-generated.

SurveyNet, a new deep learning model, automates survey data entry by combining Optical Character Recognition (OCR) and Optical Mark Recognition (OMR). This unified framework accurately digitizes handwritten text and diverse markings on real-world survey forms.

Keywords:
convolutional neural networks (CNN)data automationdeep learningform processinghandwritten digit recognitionimage classificationoptical character recognition (OCR)optical mark recognition (OMR)real-world datasetsurvey digitization

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Area of Science:

  • Computer Science
  • Data Science
  • Artificial Intelligence

Background:

  • Manual data entry from survey sheets is a significant bottleneck in research and public policy.
  • Existing Optical Character Recognition (OCR) and Optical Mark Recognition (OMR) systems are often separate and struggle with real-world survey data variability.

Purpose of the Study:

  • To develop a unified deep learning framework, SurveyNet, for automated digitization of complex survey responses.
  • To integrate Optical Character Recognition (OCR) and Optical Mark Recognition (OMR) capabilities into a single model.

Main Methods:

  • SurveyNet processes handwritten digits and various mark types (ticks, circles, crosses) within a single deep learning model.
  • A novel dataset, SurveySet, comprising 135 real-world survey forms, was created for training and evaluation.

Main Results:

  • SurveyNet achieved classification accuracy ranging from 50% to 97% across different tasks.
  • The model demonstrated strong performance even on small and imbalanced datasets, indicating robustness.

Conclusions:

  • SurveyNet provides a scalable solution for automating survey digitization, reducing manual errors, and enabling faster data analysis.
  • The framework has broad applicability in fields like consumer research, public health, and education.