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Add image-to-text task category and link paper

#2
by nielsr HF Staff - opened
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  1. README.md +12 -10
README.md CHANGED
@@ -1,10 +1,12 @@
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  ---
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  license: apache-2.0
 
 
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  ---
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  <h1 align="center">MORE: A <u><strong>M</strong></u>ultilingual D<u><strong>o</strong></u>cument Pa<u><strong>r</strong></u>sing B<u><strong>e</strong></u>nchmark and Evaluation</h1>
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- <p align="center"><nobr><a href="https://openreview.net/pdf?id=ov240fehF6"><img alt="Paper: ICML2026" src="https://img.shields.io/badge/Paper-ICML2026-b31b1b.svg"></a>&nbsp;<a href="https://github.com/zimoqingfeng/MORE"><img alt="GitHub: MORE" src="https://img.shields.io/badge/GitHub-MORE-181717.svg?logo=github"></a></nobr></p>
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  <p align="center">
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  <a href="#overview">🌍 Overview</a> |
@@ -333,39 +335,39 @@ MORE follows the OmniDocBench v1.5 evaluation protocol and extends it to code an
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  Sequential tasks use normalized edit distance:
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- $$
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  \begin{aligned}
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  \mathrm{NED}
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  &= 1 - \frac{1}{N} \sum_{i=1}^{N}
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  \frac{\mathrm{EditDist}(P_i, G_i)}
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  {\max(\lvert P_i\rvert, \lvert G_i\rvert)}
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  \end{aligned}
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- $$
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  Formula recognition uses Character Detection Matching:
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- $$
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  \begin{aligned}
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  \mathrm{CDM}
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  &= \frac{1}{N} \sum_{i=1}^{N}
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  \frac{2 \cdot TP_i}{2 \cdot TP_i + FP_i + FN_i}
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  \end{aligned}
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- $$
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  Table recognition uses TEDS:
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- $$
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  \begin{aligned}
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  \mathrm{TEDS}
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  &= 1 - \frac{1}{N} \sum_{i=1}^{N}
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  \frac{\mathrm{TreeEditDist}(T_{p,i}, T_{g,i})}
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  {\max(\lvert T_{p,i}\rvert, \lvert T_{g,i}\rvert)}
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  \end{aligned}
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- $$
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  The overall score is the arithmetic mean of the six task scores:
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- $$
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  \begin{aligned}
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  \mathrm{Overall}
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  &= \frac{1}{6}
@@ -375,7 +377,7 @@ $$
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  + \sum_{k \in \mathcal{S}} \mathrm{NED}_k
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  \right)
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  \end{aligned}
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- $$
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  <a id="leaderboard"></a>
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@@ -449,4 +451,4 @@ We sincerely hope MORE is useful for your research. If it helps your work, we wo
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  booktitle = {International Conference on Machine Learning},
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  year = {2026}
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  }
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- ```
 
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  ---
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  license: apache-2.0
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+ task_categories:
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+ - image-to-text
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  ---
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  <h1 align="center">MORE: A <u><strong>M</strong></u>ultilingual D<u><strong>o</strong></u>cument Pa<u><strong>r</strong></u>sing B<u><strong>e</strong></u>nchmark and Evaluation</h1>
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+ <p align="center"><nobr><a href="https://huggingface.co/papers/2607.02956"><img alt="Paper" src="https://img.shields.io/badge/HF-Paper-blue.svg"></a>&nbsp;<a href="https://openreview.net/pdf?id=ov240fehF6"><img alt="Paper: ICML2026" src="https://img.shields.io/badge/Paper-ICML2026-b31b1b.svg"></a>&nbsp;<a href="https://github.com/zimoqingfeng/MORE"><img alt="GitHub: MORE" src="https://img.shields.io/badge/GitHub-MORE-181717.svg?logo=github"></a></nobr></p>
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  <p align="center">
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  <a href="#overview">🌍 Overview</a> |
 
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  Sequential tasks use normalized edit distance:
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+ ```math
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  \begin{aligned}
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  \mathrm{NED}
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  &= 1 - \frac{1}{N} \sum_{i=1}^{N}
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  \frac{\mathrm{EditDist}(P_i, G_i)}
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  {\max(\lvert P_i\rvert, \lvert G_i\rvert)}
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  \end{aligned}
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+ ```
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  Formula recognition uses Character Detection Matching:
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+ ```math
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  \begin{aligned}
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  \mathrm{CDM}
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  &= \frac{1}{N} \sum_{i=1}^{N}
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  \frac{2 \cdot TP_i}{2 \cdot TP_i + FP_i + FN_i}
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  \end{aligned}
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+ ```
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  Table recognition uses TEDS:
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+ ```math
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  \begin{aligned}
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  \mathrm{TEDS}
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  &= 1 - \frac{1}{N} \sum_{i=1}^{N}
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  \frac{\mathrm{TreeEditDist}(T_{p,i}, T_{g,i})}
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  {\max(\lvert T_{p,i}\rvert, \lvert T_{g,i}\rvert)}
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  \end{aligned}
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+ ```
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  The overall score is the arithmetic mean of the six task scores:
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+ ```math
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  \begin{aligned}
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  \mathrm{Overall}
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  &= \frac{1}{6}
 
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  + \sum_{k \in \mathcal{S}} \mathrm{NED}_k
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  \right)
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  \end{aligned}
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+ ```
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  <a id="leaderboard"></a>
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  booktitle = {International Conference on Machine Learning},
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  year = {2026}
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  }
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+ ```