# Document Image Binarization with Fully Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1708.03276 · HTML: https://mlsanity.com/paper/1708.03276/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2017.

## Built on by (most cited first, 25 of 25)

- [DocEnTr: An End-to-End Document Image Enhancement Transformer](https://mlsanity.com/paper/2201.10252/) (2022)
- [DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks](https://mlsanity.com/paper/2405.04408/) (2024)
- [GDB: Gated convolutions-based Document Binarization](https://mlsanity.com/paper/2302.02073/) (2023)
- [A Survey on Deep learning based Document Image Enhancement](https://mlsanity.com/paper/2112.02719/) (2021)
- [Light-weight Document Image Cleanup using Perceptual Loss](https://mlsanity.com/paper/2105.09076/) (2021)
- [Three-stage binarization of color document images based on discrete wavelet transform and generative adversarial networks](https://mlsanity.com/paper/2211.16098/) (2022)
- [DocBinFormer: A Two-Level Transformer Network for Effective Document Image Binarization](https://mlsanity.com/paper/2312.03568/) (2023)
- [DocStormer: Revitalizing Multi-Degraded Colored Document Images to Pristine PDF](https://mlsanity.com/paper/2310.17910/) (2023)
- [Binarizing Documents by Leveraging both Space and Frequency](https://mlsanity.com/paper/2404.17243/) (2024)
- [CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization](https://mlsanity.com/paper/2305.17420/) (2023)
- [Volumetric Fast Fourier Convolution for Detecting Ink on the Carbonized Herculaneum Papyri](https://mlsanity.com/paper/2308.05070/) (2023)
- [A Layer-Wise Tokens-to-Token Transformer Network for Improved Historical Document Image Enhancement](https://mlsanity.com/paper/2312.03946/) (2023)
- [MFE-GAN: Efficient GAN-based Framework for Document Image Enhancement and Binarization with Multi-scale Feature Extraction](https://mlsanity.com/paper/2512.14114/) (2025)
- [LS-HDIB: A Large Scale Handwritten Document Image Binarization Dataset](https://mlsanity.com/paper/2101.11674/) (2021)
- [DELINE8K: A Synthetic Data Pipeline for the Semantic Segmentation of Historical Documents](https://mlsanity.com/paper/2404.19259/) (2024)
- [Unveiling Text in Challenging Stone Inscriptions: A Character-Context-Aware Patching Strategy for Binarization](https://mlsanity.com/paper/2601.03609/) (2026)
- [Document Image Binarization in JPEG Compressed Domain using Dual Discriminator Generative Adversarial Networks](https://mlsanity.com/paper/2209.05921/) (2022)
- [Variational Augmentation for Enhancing Historical Document Image Binarization](https://mlsanity.com/paper/2211.06581/) (2022)
- [Leveraging Semantic Segmentation Masks with Embeddings for Fine-Grained Form Classification](https://mlsanity.com/paper/2405.14162/) (2024)
- [Adaptive Multi Scale Document Binarisation Using Vision Mamba](https://mlsanity.com/paper/2410.22811/) (2024)
- [Benchmarking OCR Pipelines with Adaptive Enhancement for Multi-Domain Retail Bill Digitization](https://mlsanity.com/paper/2604.25176/) (2026)
- [Bridging the Sim-to-Real Gap in Semiconductor Visual Program Synthesis via Input Binarization](https://mlsanity.com/paper/2606.02434/) (2026)
- [ERN-Net : Evolving Reason Node-Net for Document Binarization](https://mlsanity.com/paper/2606.11710/) (2026)
- [DR-Mamba: Automatic Inference-Time Domain Adaptation for Document Image Binarization via Sample-Conditioned Detail-Background Suppression](https://mlsanity.com/paper/2606.22625/) (2026)
- [ENet-GP: Unified Document Image Restoration](https://mlsanity.com/paper/2609.33758/) (2026)

## Abstract (arXiv)

Binarization of degraded historical manuscript images is an important pre-processing step for many document processing tasks. We formulate binarization as a pixel classification learning task and apply a novel Fully Convolutional Network (FCN) architecture that operates at multiple image scales, including full resolution. The FCN is trained to optimize a continuous version of the Pseudo F-measure metric and an ensemble of FCNs outperform the competition winners on 4 of 7 DIBCO competitions. This same binarization technique can also be applied to different domains such as Palm Leaf Manuscripts with good performance. We analyze the performance of the proposed model w.r.t. the architectural hyperparameters, size and diversity of training data, and the input features chosen.