qurator-spk/sbb_binarization
1
0
Fork 0
mirror of https://github.com/qurator-spk/sbb_binarization.git synced 2025-08-07 09:09:58 +02:00
Code Issues Releases Wiki Activity

Update README

Browse source 
* recommend cropping (fix #49)
* document huggingface saved_model
This commit is contained in:
Clemens Neudecker 2022-10-11 11:37:00 +02:00 • committed by GitHub
parent f11d0b0bf7
commit 56ccb39539
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
1 changed files with 10 additions and 4 deletions
Download patch file Download diff file Expand all files Collapse all files

14
README.md
View file

@ -8,7 +8,7 @@
## Introduction ## Introduction
This tool performs document image binarization using trained models. The method is based on [Calvo-Zaragoza and Gallego, 2018](https://arxiv.org/abs/1706.10241). This tool performs document image binarization using a trained ResNet50-UNet model.
## Installation ## Installation
@ -18,10 +18,14 @@ Clone the repository, enter it and run
### Models ### Models
Pre-trained models can be downloaded from here: Pre-trained models in `h5` format can be downloaded from here:
https://qurator-data.de/sbb_binarization/ https://qurator-data.de/sbb_binarization/
We also provide a Tensorflow `saved_model` via Huggingface:
https://huggingface.co/SBB/sbb_binarization
## Usage ## Usage
```sh ```sh
@ -32,9 +36,11 @@ sbb_binarize \
<output image> <output image>
``` ```
**Note** In virtually all cases, applying the `--patches` flag will improve the quality of results. In virtually all cases, applying the `--patches` flag will improve the quality of results.
Example Images containing a lot of border noise (black pixels) should be cropped beforehand to improve the quality of results.
### Example
```sh ```sh
sbb_binarize --patches -m /path/to/models/ myimage.tif myimage-bin.tif sbb_binarize --patches -m /path/to/models/ myimage.tif myimage-bin.tif