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---
language:
- en
tags:
- Manga
- Object Detection
- OCR
- Clustering
- Diarisation
---
<style>
.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 */
}
</style>
<div class="title-container">
<div class="title">
The <span>Ma</span>n<span>g</span>a Wh<span>i</span>sperer
</div>
<div class="subheading">
Automatically Generating Transcriptions for Comics
</div>
<div class="authors">
Ragav Sachdeva and Andrew Zisserman
</div>
<div class="affil">
University of Oxford
</div>
<div style="display: flex;">
<a href="https://arxiv.org/abs/2401.10224"><img alt="Static Badge" src="https://img.shields.io/badge/arXiv-2401.10224-blue"></a>
 
<img alt="Dynamic JSON Badge" src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fhuggingface.co%2Fapi%2Fmodels%2Fragavsachdeva%2Fmagi%3Fexpand%255B%255D%3Ddownloads%26expand%255B%255D%3DdownloadsAllTime&query=%24.downloadsAllTime&label=%F0%9F%A4%97%20Downloads">
</div>
</div>
# Usage
```python
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}
}
``` |