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arXiv · 2606.11710

ERN-Net : Evolving Reason Node-Net for Document Binarization

Hsin-Jui Pan·Sheng-Wei Chan·Jen-Shiung Chiang
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Abstract

This paper presents ERN-Net, an Evolving Reason Node-Net for efficient document image binarization. ERN-Net enhances degradation-sensitive regions, such as faint strokes, broken characters, and noisy backgrounds, through evolving reason nodes and multi-scale reasoning. We further compare ResNet-101, ConvNeXt-Tiny, and ConvNeXt-Base, and find that ConvNeXt-Tiny provides the best practical trade-off between accuracy and memory usage. In addition, DIBCO-based pretraining improves binarization performance without increasing model memory consumption, requiring only about 1.5 additional training hours. Experiments on DIBCO-style benchmarks show that ERN-Net is effective under low-data and low-memory settings.

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Hsin-Jui Pan, Sheng-Wei Chan, Jen-Shiung Chiang. 2026-06-10. ERN-Net : Evolving Reason Node-Net for Document Binarization. https://arxiv.org/abs/2606.11710

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