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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">
Ta<span>il</span>s Tell Ta<span>le</span>s
</div>
<div class="subheading">
Chapter-Wide Manga Transcriptions With Character Names
</div>
<div class="authors">
Ragav Sachdeva, Gyungin Shin and Andrew Zisserman
</div>
<div class="affil">
University of Oxford
</div>
<div style="display: flex;">
<a href="https://arxiv.org/abs/2408.00298"><img alt="Static Badge" src="https://img.shields.io/badge/arXiv-2408.00298-blue"></a>
 
<img alt="Dynamic JSON Badge" src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fhuggingface.co%2Fapi%2Fmodels%2Fragavsachdeva%2Fmagiv2%3Fexpand%255B%255D%3Ddownloads%26expand%255B%255D%3DdownloadsAllTime&query=%24.downloadsAllTime&label=%F0%9F%A4%97%20Downloads">
</div>
</div>
# Usage
```python
from PIL import Image
import numpy as np
from transformers import AutoModel
import torch
model = AutoModel.from_pretrained("ragavsachdeva/magiv2", trust_remote_code=True).cuda().eval()
def read_image(path_to_image):
with open(path_to_image, "rb") as file:
image = Image.open(file).convert("L").convert("RGB")
image = np.array(image)
return image
chapter_pages = ["page1.png", "page2.png", "page3.png" ...]
character_bank = {
"images": ["char1.png", "char2.png", "char3.png", "char4.png" ...],
"names": ["Luffy", "Sanji", "Zoro", "Ussop" ...]
}
chapter_pages = [read_image(x) for x in chapter_pages]
character_bank["images"] = [read_image(x) for x in character_bank["images"]]
with torch.no_grad():
per_page_results = model.do_chapter_wide_prediction(chapter_pages, character_bank, use_tqdm=True, do_ocr=True)
transcript = []
for i, (image, page_result) in enumerate(zip(chapter_pages, per_page_results)):
model.visualise_single_image_prediction(image, page_result, f"page_{i}.png")
speaker_name = {
text_idx: page_result["character_names"][char_idx] for text_idx, char_idx in page_result["text_character_associations"]
}
for j in range(len(page_result["ocr"])):
if not page_result["is_essential_text"][j]:
continue
name = speaker_name.get(j, "unsure")
transcript.append(f"<{name}>: {page_result['ocr'][j]}")
with open(f"transcript.txt", "w") as fh:
for line in transcript:
fh.write(line + "\n")
```
# 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{magiv2,
title={Tails Tell Tales: Chapter-Wide Manga Transcriptions with Character Names},
author={Ragav Sachdeva and Gyungin Shin and Andrew Zisserman},
year={2024},
eprint={2408.00298},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2408.00298},
}
``` |