From 2460a0836bdd43b1e3efa678da87fd4a0c503a0a Mon Sep 17 00:00:00 2001 From: NiharikaVadlamudi Date: Mon, 26 Aug 2024 01:14:43 +0530 Subject: [PATCH] GithubPages --- index.html | 283 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 283 insertions(+) create mode 100644 index.html diff --git a/index.html b/index.html new file mode 100644 index 0000000..ad86d76 --- /dev/null +++ b/index.html @@ -0,0 +1,283 @@ + + + + + + + + LineTR:Unified Text Line Segmentation for Challenging Palm Leaf Manuscripts" + + + + + + + + + + + + + + + + + + + + + + + + + + +
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LineTR

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+ + Vaibhav Agrawal1, + + Niharika Vadlamudi1, + + Muhammad Waseem1, + + + Amal Joseph1, + + + Sreenya Chitluri1, + + + Ravi Kiran Sarvadevabhatla1, + +
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+ 1International Institute of Information Technology,Hyderabad +
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+ LineTR works on palm leaf manuscripts in + an dataset agnostic manner. +

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Abstract

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+ 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. +

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+ 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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Video

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Network Architecture

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+ 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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BibTeX

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@article{vaibav2024linetr,
+  author    = {TBD},
+  title     = {LineTR:Unified Text Line Segmentation for Challenging Palm Leaf Manuscripts},
+  journal   = {ICPR},
+  year      = {2024},
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Contact

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+ If you have any question, please contact Dr. Ravi Kiran Sarvadevabhatla at ravi.kiran@iiit.ac.in. +

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