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<!DOCTYPE html> | ||
<html> | ||
<head> | ||
<meta charset="utf-8"> | ||
<meta name="description" | ||
content="LineTR: Unified Text Line Segmentation for Challenging Palm Leaf Manuscripts"> | ||
<meta name="keywords" content="Zero Shot,Line Segmentation,Palm Manuscripts"> | ||
<meta name="viewport" content="width=device-width, initial-scale=1"> | ||
<title>LineTR:Unified Text Line Segmentation for Challenging Palm Leaf Manuscripts"</title> | ||
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<h1 class="title is-1 publication-title">LineTR</h1> | ||
<div class="is-size-5 publication-authors"> | ||
<span class="author-block"> | ||
<a href="https://keunhong.com">Vaibhav Agrawal</a><sup>1</sup>,</span> | ||
<span class="author-block"> | ||
<a href="https://www.linkedin.com/in/niharika-vadlamudi/">Niharika Vadlamudi</a><sup>1</sup>,</span> | ||
<span class="author-block"> | ||
<a href="https://www.linkedin.com/in/hwaseem04/">Muhammad Waseem</a><sup>1</sup>, | ||
</span> | ||
<span class="author-block"> | ||
<a href="https://www.linkedin.com/in/amaljoseph/">Amal Joseph</a><sup>1</sup>, | ||
</span> | ||
<span class="author-block"> | ||
<a href="https://www.danbgoldman.com">Sreenya Chitluri</a><sup>1</sup>, | ||
</span> | ||
<span class="author-block"> | ||
<a href="https://ravika.github.io">Ravi Kiran Sarvadevabhatla</a><sup>1</sup>, | ||
</span> | ||
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<div class="is-size-5 publication-authors"> | ||
<span class="author-block"><sup>1</sup>International Institute of Information Technology,Hyderabad</span> | ||
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</section> | ||
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<section class="hero teaser"> | ||
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<div class="hero-body"> | ||
<video id="teaser" autoplay muted loop playsinline height="100%"> | ||
<source src="./static/videos/sf_video.mp4" | ||
type="video/mp4"> | ||
</video> | ||
<h2 class="subtitle has-text-centered"> | ||
<span class="dnerf">LineTR</span> works on palm leaf manuscripts in | ||
an dataset agnostic manner. | ||
</h2> | ||
</div> | ||
</div> | ||
</section> | ||
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<section class="section"> | ||
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<!-- Abstract. --> | ||
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<div class="column is-four-fifths"> | ||
<h2 class="title is-3">Abstract</h2> | ||
<div class="content has-text-justified"> | ||
<p> | ||
We propose LineTR, a novel two-stage line segmentation approach which can process a diverse variety of challenging handwritten documents in a unified, | ||
dataset-agnostic manner. | ||
</p> | ||
<p> | ||
Historical manuscripts pose significant challenges for line segmentation due to their diverse sizes, scripts, and appearances. | ||
Traditional methods often rely on dataset-specific processing or training per-dataset models, limiting scalability and maintainability. | ||
In the first stage, LineTR processes context-adaptive image patches using a DETR-style network to generate parametric representations of text lines and a hybrid CNN-transformer network to create a text energy map. | ||
A robust post-processing procedure converts these into document-level scribbles. | ||
In the second stage, these scribbles and the text energy map are used to generate precise polygons enclosing the text lines. | ||
Experimental results demonstrate that LineTR achieves superior line segmentation with a single model and performs well in zero-shot inference on the new datasets. | ||
</p> | ||
</div> | ||
</div> | ||
</div> | ||
<!--/ Abstract. --> | ||
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<!-- Paper video. --> | ||
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<h2 class="title is-3">Video</h2> | ||
<div class="publication-video"> | ||
<iframe src="https://www.youtube.com/watch?v=38S56ottFQ4&list=PLGfNvK0w_pZM4o0iwZ3DM1WdjaAtLlifg" | ||
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</section> | ||
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<section class="section"> | ||
<div class="container is-max-desktop"> | ||
<!-- Network Architecture . --> | ||
<div class="columns is-centered has-text-centered"> | ||
<div class="column is-four-fifths"> | ||
<h2 class="title is-3">Network Architecture</h2> | ||
<div class="content has-text-justified"> | ||
<p> | ||
Historical manuscripts pose significant challenges for line segmentation due to their diverse sizes, scripts, and appearances. | ||
Traditional methods often rely on dataset-specific processing or training per-dataset models, limiting scalability and maintainability. | ||
In the first stage, LineTR processes context-adaptive image patches using a DETR-style network to generate parametric representations of text lines and a hybrid CNN-transformer network to create a text energy map. | ||
A robust post-processing procedure converts these into document-level scribbles. | ||
In the second stage, these scribbles and the text energy map are used to generate precise polygons enclosing the text lines. | ||
Experimental results demonstrate that LineTR achieves superior line segmentation with a single model and performs well in zero-shot inference on the new datasets. | ||
</p> | ||
</div> | ||
</div> | ||
</div> | ||
</section> | ||
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<section class="section" id="BibTeX"> | ||
<div class="container is-max-desktop content"> | ||
<h2 class="title">BibTeX</h2> | ||
<pre><code>@article{vaibav2024linetr, | ||
author = {TBD}, | ||
title = {LineTR:Unified Text Line Segmentation for Challenging Palm Leaf Manuscripts}, | ||
journal = {ICPR}, | ||
year = {2024}, | ||
}</code></pre> | ||
</div> | ||
</section> | ||
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<section class="section" id="Contact"> | ||
<div class="container is-max-desktop content"> | ||
<h2 class="title">Contact</h2> | ||
<div class="content has-text-justified"> | ||
<p> | ||
If you have any question, please contact Dr. Ravi Kiran Sarvadevabhatla at [email protected]. | ||
</p> | ||
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