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Redacted text detection using neural image segmentation methods.

Ruben van Heusden¹, Kaj Meijer¹, Maarten Marx¹

¹Information Retrieval Lab, University of Amsterdam, Amsterdam, The Netherlands.

International Journal on Document Analysis and Recognition (Online)

|December 1, 2025

Summary

This study introduces automated methods to detect redactions in documents, crucial for privacy and transparency. The Mask R-CNN model achieved high precision and recall, even with varied redaction types and non-redacted pages.

Keywords:

Instance Segmentation Mask R-CNN Mask2Former Text Redaction

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Area of Science:

Computer Science
Information Science
Digital Forensics

Background:

Redaction of sensitive information is standard practice in legal and public disclosure contexts.
Automated redaction detection aids in analyzing redaction prevalence and effectiveness.

Purpose of the Study:

To evaluate neural network models for automatic redaction detection.
To compare performance against a rule-based approach.

Main Methods:

Implementation and testing of Mask R-CNN and Mask2Former neural network models.
Comparison with a rule-based model utilizing optical character recognition and morphological operations.

Main Results:

Mask R-CNN demonstrated superior performance with a recall of 0.94 and precision of 0.96 on a diverse dataset.
Performance remained robust with minimal degradation when non-redacted pages were included.
Mask2Former exhibited the highest robustness against non-redacted inputs, yielding fewer false positives.

Conclusions:

Neural network approaches, particularly Mask R-CNN, are effective for automated redaction detection.
The developed methods offer practical applications for document analysis and privacy auditing.
Further research can refine models for enhanced accuracy and robustness across various redaction techniques.