.title-container { display: flex; flex-direction: column; /* Stack elements vertically */ justify-content: center; align-items: center; } .title { font-size: 2em; text-align: center; color: #333; font-family: 'Comic Sans MS', cursive; /* Use Comic Sans MS font */ text-transform: uppercase; letter-spacing: 0.1em; padding: 0.5em 0 0.2em; background: transparent; } .title span { background: -webkit-linear-gradient(45deg, #6495ED, #4169E1); /* Blue gradient */ -webkit-background-clip: text; -webkit-text-fill-color: transparent; } .subheading { font-size: 1.5em; /* Adjust the size as needed */ text-align: center; color: #555; /* Adjust the color as needed */ font-family: 'Comic Sans MS', cursive; /* Use Comic Sans MS font */ } .authors { font-size: 1em; /* Adjust the size as needed */ text-align: center; color: #777; /* Adjust the color as needed */ font-family: 'Comic Sans MS', cursive; /* Use Comic Sans MS font */ padding-top: 1em; } .affil { font-size: 1em; /* Adjust the size as needed */ text-align: center; color: #777; /* Adjust the color as needed */ font-family: 'Comic Sans MS', cursive; /* Use Comic Sans MS font */ }

Usage

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")

License and Citation

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: ragavsachdeva [dot] github [dot] io

@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}
}