docentr

Maintainer: cjwbw

Total Score

3

Last updated 6/9/2024
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Paper LinkView on Arxiv

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Model overview

The docentr model is an end-to-end document image enhancement transformer developed by cjwbw. It is a PyTorch implementation of the paper "DocEnTr: An End-to-End Document Image Enhancement Transformer" and is built on top of the vit-pytorch vision transformers library. The model is designed to enhance and binarize degraded document images, as demonstrated in the provided examples.

Model inputs and outputs

The docentr model takes an image as input and produces an enhanced, binarized output image. The input image can be a degraded or low-quality document, and the model aims to improve its visual quality by performing tasks such as binarization, noise removal, and contrast enhancement.

Inputs

  • image: The input image, which should be in a valid image format (e.g., PNG, JPEG).

Outputs

  • Output: The enhanced, binarized output image.

Capabilities

The docentr model is capable of performing end-to-end document image enhancement, including binarization, noise removal, and contrast improvement. It can be used to improve the visual quality of degraded or low-quality document images, making them more readable and easier to process. The model has shown promising results on benchmark datasets such as DIBCO, H-DIBCO, and PALM.

What can I use it for?

The docentr model can be useful for a variety of applications that involve processing and analyzing document images, such as optical character recognition (OCR), document archiving, and image-based document retrieval. By enhancing the quality of the input images, the model can help improve the accuracy and reliability of downstream tasks. Additionally, the model's capabilities can be leveraged in projects related to document digitization, historical document restoration, and automated document processing workflows.

Things to try

You can experiment with the docentr model by testing it on your own degraded document images and observing the binarization and enhancement results. The model is also available as a pre-trained Replicate model, which you can use to quickly apply the image enhancement without training the model yourself. Additionally, you can explore the provided demo notebook to gain a better understanding of how to use the model and customize its configurations.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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