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NiharikaVadlamudi committed Aug 31, 2024
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Expand Up @@ -121,9 +121,9 @@ <h1 class="title is-1 publication-title">LineTR</h1>
<a href="https://drive.google.com/file/d/1UU_4irR3m8IuuzuOXYl35eK6UbD2d3tH/view?usp=sharing"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
<i class="fas fa-file-pdf"></i>
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<span>arXiv</span>
<span>Supplementary</span>
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</section>


<section class="section">
<div class="container is-max-desktop">
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<div class="columns is-centered has-text-centered">
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<h2 class="title is-3">Introduction</h2>
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<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.
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</section>

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<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
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