DocBinFormer: A Two-Level Transformer Network for Effective Document
  Image Binarization
Risab Biswas Swalpa Kumar Roy Ning Wang Umapada Pal Guang-Bin Huang
In real life, various degradation scenarios exist that might damage document
images, making it harder to recognize and analyze them, thus binarization is a
fundamental and crucial step for achieving the most optimal performance in any
document analysis task. We propose DocBinFormer (Document Binarization
Transformer), a novel two-level vision transformer (TL-ViT) architecture based
on vision transformers for effective document image binarization. The presented
architecture employs a two-level transformer encoder to effectively capture
both global and local feature representation from the input images. These
complimentary bi-level features are exploited for efficient document image
binarization, resulting in improved results for system-generated as well as
handwritten document images in a comprehensive approach. With the absence of
convolutional layers, the transformer encoder uses the pixel patches and
sub-patches along with their positional information to operate directly on
them, while the decoder generates a clean (binarized) output image from the
latent representation of the patches. Instead of using a simple vision
transformer block to extract information from the image patches, the proposed
architecture uses two transformer blocks for greater coverage of the extracted
feature space on a global and local scale. The encoded feature representation
is used by the decoder block to generate the corresponding binarized output.
Extensive experiments on a variety of DIBCO and H-DIBCO benchmarks show that
the proposed model outperforms state-of-the-art techniques on four metrics. The
source code will be made available at
https://github.com/RisabBiswas/DocBinFormer.