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OCR-VQGAN @WACV 2023 🏝️

OCR-VQGAN: Taming Text-within-Image Generation

Juan A. Rodríguez, David Vázquez, Issam Laradji, Marco Pedersoli, Pau Rodríguez

Computer Vision Center, Autonomous University of Barcelona

ServiceNow Research, Montréal, Canada

ÉTS Montreal, University of Québec

OCR-VQGAN is an image encoder designed to generate images that display clear and readable text. We propose to add an OCR perceptual loss term to the overall VQGAN loss, that encourages the learned discrete latent space to encode text patterns (i.e. learn rich latent representations to decode clear text-within-images).

We experiment with OCR-VQGAN in and a novel dataset of images of figures and diagrams from research papers, called Paper2Fig100k dataset. We find that using OCR-VQGAN to encode images in Paper2Fig100k results in much better figure reconstructions.

This code is adapted from VQGAN at CompVis/taming-transformers, and CompVis/stable-diffusion. The OCR detector model used in OCR Perceptual loss is the CRAFT model from clovaai/CRAFT-pytorch.

Abstract

Synthetic image generation has recently experienced significant improvements in domains such as natural image or art generation. However, the problem of figure and diagram generation remains unexplored. A challenging aspect of generating figures and diagrams is effectively rendering readable texts within the images. To alleviate this problem, we present OCR-VQGAN, an image encoder, and decoder that leverages OCR pre-trained features to optimize a text perceptual loss, encouraging the architecture to preserve high-fidelity text and diagram structure. To explore our approach, we introduce the Paper2Fig100k dataset, with over 100k images of figures and texts from research papers. The figures show architecture diagrams and methodologies of articles available at arXiv.org from fields like artificial intelligence and computer vision. Figures usually include text and discrete objects, e.g., boxes in a diagram, with lines and arrows that connect them. We demonstrate the superiority of our method by conducting several experiments on the task of figure reconstruction. Additionally, we explore the qualitative and quantitative impact of weighting different perceptual metrics in the overall loss function.

Installation

Create a conda environment named ocr-vqgan, and activate with:

conda env create -f environment.yaml
conda activate ocr-vqgan
pip install -e .

How to use OCR Perceptual loss and OCR Similarity

Because we are working with images of figures (i.e., images are non-natural), a VGG perceptual loss (LPIPS) is not enough to effectively encode and decode clear texts and sharp diagrams. We propose an additional OCR perceptual loss to encourage vqgan to learn a rich latent space and reconstruct clear and readable text-within-images.

The OCR perceptual loss can be computed as follows. You can pass a pair of input and reconstructed images (using any type of image encoder/decoder):

from taming.modules.losses.lpips import OCR_CRAFT_LPIPS
from PIL import Image
import numpy as np
import torchvision.transforms as T
import torch

def get_image_tensor(image_path):
    image = Image.open(image_path)
    if not image.mode == "RGB":
        image = image.convert("RGB")
    image = np.array(image).astype(np.uint8)
    image = (image/127.5 - 1.0).astype(np.float32)
    return torch.unsqueeze(T.ToTensor()(image), 0)

# Load image and reconstruction to tensors
input_path = 'assets/original.png'
recons_path = 'assets/reconstruction.png'

input_tensor = get_image_tensor(input_path).cuda()
rec_tensor = get_image_tensor(recons_path).cuda()

OCR_perceptual_loss = OCR_CRAFT_LPIPS().eval()
OCR_perceptual_loss.cuda()

ocr_sim = OCR_perceptual_loss(input_tensor, rec_tensor)

Our OCR-VQGAN method uses OCR perceptual loss as an additional term in the overall VQGAN loss (see VQLPIPSWithDiscriminatorOCR).

Training OCR-VQGANs

Logs and checkpoints for experiments are saved into a logs directory. By default, this directory will be created inside the project, but we recommend passing the argument -l dir_path with a path where you have sufficient disk space.

Download Paper2Fig100k dataset

We train our models using Paper2Fig100k dataset, that can be downloaded here. Once downloaded, you will find the following structure:

📂Paper2Fig100k/
├── 📂figures
│   ├── 🖼️1001.1968v1-Figure1-1.png
│   ├── 🖼️1001.1988v1-Figure1-1.png
│   ├── ...
├── 📜paper2fig_train.json
├── 📜paper2fig_test.json

The directory figures contains all images in the dataset, and the train and test JSON files define data about each figure (id, captions, etc.). Run the following command to prepare Paper2Figure100k samples for the OCR-VQGAN training:

python scripts/parse_paper2fig1_img_to_VQGAN.py --path <path_Paper2Fig100k_root>

Download ICDAR 13

We also use ICDAR13 to evaluate OCR-VQGAN. Download ICDAR13 dataset(train and test sets). Create a root directory ICDAR13 and add both downloaded sets.

📂ICDAR13/
├── 📂Challenge2_Test_Task12_Images
├── 📂Challenge2_Training_Task12_Images

Run the following command to prepare images for evaluation of ICDAR13 with OCR-VQGAN.

python scripts/parse_ICDAR2013_img_to_VQGAN.py --path <path_ICDAR13_dataset>

This will create a .txt file with the paths of the images in ICDAR13 (we unify both splits for validation).

Training OCR-VQGAN from scratch

Create a new configuration for your model using a config.yaml file, or use one from the folder configs. Using the config file with the argument --base will create new experiment directory using the defined base configuration, to store checkpoints and configs.

You need to modify the training_images_list_file and test_images_list_file inside the config.yaml file (inside data), to point at the .txt files that contain paths to images:

data:
  target: main.DataModuleFromConfig
  params:
    ...
    train:
      target: taming.data.custom.CustomTrain
      params:
        training_images_list_file: <data_path>/paper2fig_train.txt
        ...
    validation:
      target: taming.data.custom.CustomTest
      params:
        test_images_list_file: <data_path>/paper2fig1_img_test.txt
        ...
    test:
      target: taming.data.custom.CustomTest
      params:
        test_images_list_file: <data_path>/paper2fig1_img_test.txt
        ...

Then run the following command to start training. You may need to configure wandb:

python main.py --base configs/<config_spec>.yaml --logdir path_to_logdir -t --gpus 0, -p <project_name>

Fine-tuning pre-trained VQGANs with Paper2Fig100k 🚀

You can also start with VQGAN pre-trained weights and fine-tune the model with figures from PaperFig100k. There are are several VQGAN pre-trained models in this model zoo. For instance, we will resume from vqgan_imagenet_16384 model available here. The steps are the following:

1. Create a directory for the new experiment. Create the configs and checkpoints directories, and add the ocr-vqgan/configs/ocr-vqgan-imagenet-16384.yaml and last.ckpt as,

📂vqgan_imagenet_16384_paper2fig/
├── 📂configs
|   ├── 📜ocr-vqgan-imagenet-16384.yaml
├── 📂checkponts
|   ├── 📜last.ckpt

2. Running the following command will automatically load the last.ckpt weights:

python main.py -r <path>/vqgan_imagenet_16384_paper2fig -t --gpus 0, -p <project_name>

or resume from a specific checkpont with:

python main.py -r <path>/model.ckpt -t --gpus 0,

NOTE: The first time that the training is executed, it will crash because the OCR weights are not in the pre-trained model. However, during the crash, it will update the last.ckpt checkpoint inside checkpoints. The next run will use that checkpoint and will work fine.

Evaluation of OCR-VQGAN

The evaluation of OCR-VQGAN consists in computing quantitative metrics for LPIPS and OCR Similarity during inference (Check the proposed metric in the paper) in a test epoch. This process also stores reconstructions in a evaluation directory.

python main.py -r dir_model --gpus 0

Computing FID, SSIM and Qualitative results

We also compute FID and SSIM of the generated images with respect to the inputs. Both operations are done over the complete sets (after the test epoch performed in the past step)

Prepare test images

Before computing FID and SSIM metrics, we need to process test samples so that they are all inside a directory and center-cropped.

python prepare_eval_samples.py --image_txt_path <path to txt file> --store_path <path_output_dir>

where --image_txt_path indicates where the txt file is located and --store_path defines the folder to store results.

Compute FID

FID is a metric to measure the similarity of two sets of images in terms of their data distribution. It is computed using full batches of images, not one by one. FID extracts InceptionV3 features of all the images, and computes the similarity using the mean and stdv of the deep features. We use torch-fidelity library to compute FID between the two sets defined by --input1 and --input2.

pip install torch-fidelity #should be already installed
fidelity --gpu 0 --fid --input1 test_samples_dir --input2 evaluation_samples_dir

Compute SSIM

Similarly, we propose to compute SSIM scores by passing two sets of images (input and reconstruction sets), again defining the sets as --input1 and --input2.

python --input1 test_samples_dir --input2 evaluation_samples_dir

This script does not use GPU, but we use multiprocessing to accelerate the computation. For 20k images, and 32 CPU cores it takes around 7 minutes.

Extract qualitative results

Extract random validation samples from different models (i.e. qualitatively evaluate the same sample generation from different methods).

python generate_qualitative_results.py --test_dataset dir_original__samples\
                 --VQGAN_pretrained dir_VQVAE_samples\
                 --VQGAN_finetuned dir_VQVAE_samples\
                 --OCR_VQGAN dir_VQVAE_samples\

Results and models

We provide quantitative and qualitative results of our model, and links to download. Config files in yaml format are available at configs. The model is defined by f, the downsampling factor, Z, the discrete codebook size, and d, the model embedding size.

More details in our paper

Related work

Taming Transformers for High-Resolution Image Synthesis by Esser et al, CVPR 2021.

High-Resolution Image Synthesis with Latent Diffusion Models by Rombach et al, CVPR 2022 Oral.

Character Region Awareness for Text Detection by Baek et al, CVPR 2019.

Citation

If you use this code please cite the following paper:

@inproceedings{rodriguez2023ocr,
  title={OCR-VQGAN: Taming Text-within-Image Generation},
  author={Rodriguez, Juan A and Vazquez, David and Laradji, Issam and Pedersoli, Marco and Rodriguez, Pau},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={3689--3698},
  year={2023}
}

Contact

Juan A. Rodríguez (joanrg.ai@gmail.com). We welcome collaborators! so don't hesitate to ask us about the project.