Ragav Sachdeva, Andrew Zisserman
- The model is available at 🤗 HuggingFace Model Hub.
- Try it out for yourself using this 🤗 HuggingFace Spaces Demo (no GPU, so slow).
- Dataset info is available here.
- Basic model usage is provided below. Inspect this file for more info.
from transformers import AutoModel
import numpy as np
from PIL import Image
import torch
import os
images = [
"path_to_image1.jpg",
"path_to_image2.png",
]
def read_image_as_np_array(image_path):
with open(image_path, "rb") as file:
image = Image.open(file).convert("L").convert("RGB")
image = np.array(image)
return image
images = [read_image_as_np_array(image) for image in images]
model = AutoModel.from_pretrained("ragavsachdeva/magi", trust_remote_code=True).cuda()
with torch.no_grad():
results = model.predict_detections_and_associations(images)
text_bboxes_for_all_images = [x["texts"] for x in results]
ocr_results = model.predict_ocr(images, text_bboxes_for_all_images)
for i in range(len(images)):
model.visualise_single_image_prediction(images[i], results[i], filename=f"image_{i}.png")
model.generate_transcript_for_single_image(results[i], ocr_results[i], filename=f"transcript_{i}.txt")The provided model and datasets are available for unrestricted use in personal, research, non-commercial, and not-for-profit endeavors. For any other usage scenarios, kindly contact me via email, providing a detailed description of your requirements, to establish a tailored licensing arrangement. My contact information can be found on my website.
@misc{sachdeva2024manga,
title={The Manga Whisperer: Automatically Generating Transcriptions for Comics},
author={Ragav Sachdeva and Andrew Zisserman},
year={2024},
eprint={2401.10224},
archivePrefix={arXiv},
primaryClass={cs.CV}
}