Δoxa Binarization Framework

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Introduction

ΔBF is an image binarization framework which focuses primarily on local adaptive thresholding algorithms. In English, this means that it has the ability to turn a color or gray scale image into a black and white image. It is written in C++ but supports multiple language bindings.

Algorithms

• Otsu - "A threshold selection method from gray-level histograms", 1979.
• Bernsen - "Dynamic thresholding of gray-level images", 1986.
• Niblack - "An Introduction to Digital Image Processing", 1986.
• Sauvola - "Adaptive document image binarization", 1999.
• Wolf - "Extraction and Recognition of Artificial Text in Multimedia Documents", 2003.
• Gatos - "Adaptive degraded document image binarization", 2005. (Partial)
• NICK - "Comparison of Niblack inspired Binarization methods for ancient documents", 2009.
• AdOtsu - "A multi-scale framework for adaptive binarization of degraded document images", 2010.
• Su - "Binarization of Historical Document Images Using the Local Maximum and Minimum", 2010.
• T.R. Singh - "A New local Adaptive Thresholding Technique in Binarization", 2011.
• Bataineh - "An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows", 2011. (unreproducible)
• ISauvola - "ISauvola: Improved Sauvola’s Algorithm for Document Image Binarization", 2016.
• WAN - "Binarization of Document Image Using Optimum Threshold Modification", 2018.

Optimizations

• Shafait - "Efficient Implementation of Local Adaptive Thresholding Techniques Using Integral Images", 2008.
• Petty - An algorithm for efficiently calculating the min and max of a local window. Unpublished, 2019.
• Chan - "Memory-efficient and fast implementation of local adaptive binarization methods", 2019.
• SIMD - Supporting: SSE2, ARM NEON, WASM SIMD128

Performance Metrics

• Overall Accuracy
• F-Measure, Precision, Recall
• Pseudo F-Measure, Precision, Recall - "Performance Evaluation Methodology for Historical Document Image Binarization", 2013.
• Peak Signal-To-Noise Ratio (PSNR)
• Negative Rate Metric (NRM)
• Matthews Correlation Coefficient (MCC)
• Distance-Reciprocal Distortion Measure (DRDM) - "An Objective Distortion Measure for Binary Document Images Based on Human Visual Perception", 2002.

Native Image Support

• Portable Any-Map: PBM (P4), 8-bit PGM (P5), PPM (P6), PAM (P7)

Overview

The goal of this library is to provide the building blocks one might use to advance the state of handwritten manuscript binarization. What sets this binarization library apart is that it is intended to be used by those interested in experimenting with their own algorithms and enhancements. Instead of being relegated to MATLAB, or obfuscated by mathematics in a research paper, a lot of effort has gone into exposing these binarization techniques in an open and transparent way. A key objective in designing this framework was to make it modular and as easy to use as possible, without sacrificing speed and without depending heavily on 3rd party frameworks. This library is also heavily unit tested to help ensure quality, and to quickly spot problems after experimenting with the codebase.

Example

This short example shows you how easy it is to use ΔBF to process an image.

// Read a 32-bit color image and automatically convert to 8-bit gray scale
Image image = PNM::Read(R"(C:\MyImage.pam)");

// Use a binarization algorithm to convert it into black and white
const Parameters parameters({ {"window", 25}, {"k", 0.10} });
Image imageSauvola = Sauvola::ToBinaryImage(image, parameters);

// Save the processed image
PNM::Write(imageSauvola, R"(C:\MyImage-Sauvola.pam)");

ΔBF is incredibly light weight, being a header-only library. It can integrate easily with other 3rd party C++ frameworks like OpenCV and Qt. Examples can be found under the Demo folder.

Building

The core library is header-only and requires no build. For bindings and tests, use CMake presets:

# Build and run C++ unit tests
cmake --preset cpp-tests
cmake --build build-cpp-tests --config Release
ctest --test-dir build-cpp-tests -C Release

# Build and test Python bindings (requires Python 3.12+, nanobind)
cmake --preset python
cmake --build build-python --config Release
ctest --test-dir build-python -C Release

# Build and test WebAssembly (requires Emscripten in PATH)
cmake --preset wasm
cmake --build build-wasm --config Release
ctest --test-dir build-wasm -C Release

# Build and test MATLAB bindings (requires MATLAB)
cmake --preset matlab
cmake --build build-matlab --config Release
ctest --test-dir build-matlab -C Release

# Build and run performance benchmarks (Google Benchmark)
cmake --preset benchmarks
cmake --build build-bench --config Release
./build-bench/Doxa.Bench/doxa_bench              # Linux/Mac
./build-bench/Doxa.Bench/Release/doxa_bench.exe  # Windows

# Build everything (C++ Tests, Python, WASM)
cmake --preset all
cmake --build build --config Release
ctest --test-dir build -C Release

See Bindings/Python/README.md, Bindings/WebAssembly/README.md, and Bindings/Matlab/README.md for detailed instructions.

Performance Benchmarks

The project uses Google Benchmark for measuring runtime performance of SIMD optimizations, calculator backends, and core operations. Benchmarks are separate from unit tests to keep correctness and performance concerns independent.

# Run benchmarks long enough to lower CV %
./build-bench/Doxa.Bench/doxa_bench --benchmark_min_time=1s --benchmark_repetitions=10 --benchmark_report_aggregates_only=true

# Compare two runs (e.g., before/after a change, or across platforms)
# Requires running doxa_bench with: --benchmark_out=results.json --benchmark_out_format=json
python build-bench/_deps/googlebenchmark-src/tools/compare.py benchmarks before.json after.json

CI automatically tracks benchmark results per platform (Linux, Windows, macOS) and alerts on regressions in pull requests.

Performance Analysis

Another thing that sets ΔBF apart is its focus on binarization performance. This makes it incredibly simple to see how your changes affect the overall quality of an algorithm.

Language Bindings

• Javascript / WASM
• Python
• Matlab

Experimental WASM support has been added in order to expose ΔBF to the web, as well as NodeJs. For Python, an experimental DoxaPy library has been developed and publish on PyPi. Experimental Matlab bindings can also be built. Examples of how to use each binding are provided in the Demo folder.

A Live Demo has been created to highlight some of what ΔBF is capable of on the web.

License

CC0 - Brandon M. Petty, 2026

To the extent possible under law, the author(s) have dedicated all copyright and related and neighboring rights to this software to the public domain worldwide. This software is distributed without any warranty.

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"Freely you have received; freely give." - Matt 10:8