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UniParser
/

MMTR-Bench-Dataset

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Image-Text-to-Text

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MMTR-Bench commited on Apr 10

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@@ -23,7 +23,7 @@ To solve the task, models must recover the hidden text by relying on the remaini
 MMTR-Bench evaluates a model's ability to maintain a continuous, structured reading flow across complex multimodal layouts. The dataset is rigorously balanced across various dimensions to ensure a comprehensive evaluation of current Multimodal Large Language Models (MLLMs).
-![Dataset Overview](./png/overview.jpg)
 The distributions in the dataset highlight our multi-faceted evaluation strategy:
 * **Difficulty Level & Context Mode (a):** The dataset is categorized into four distinct difficulty levels (L1 to L4), scaling from word-level completion to complex paragraph-level reconstruction. It incorporates both Single Context (single-page) and Multi Context (multi-page) scenarios, demanding robust cross-page reasoning.
@@ -36,7 +36,7 @@ The distributions in the dataset highlight our multi-faceted evaluation strategy
 The benchmark assesses models using a specialized level-aware scoring mechanism to account for the varying complexities of L1 through L4 tasks. The inclusion of explicit reasoning ("Thinking") models reveals a significant paradigm shift in how MLLMs approach visual text reconstruction.
-![Model Benchmark Comparison](./png/model_bench.jpg)
 ### Main Results

 MMTR-Bench evaluates a model's ability to maintain a continuous, structured reading flow across complex multimodal layouts. The dataset is rigorously balanced across various dimensions to ensure a comprehensive evaluation of current Multimodal Large Language Models (MLLMs).
+![Dataset Overview](./png/overview.png)
 The distributions in the dataset highlight our multi-faceted evaluation strategy:
 * **Difficulty Level & Context Mode (a):** The dataset is categorized into four distinct difficulty levels (L1 to L4), scaling from word-level completion to complex paragraph-level reconstruction. It incorporates both Single Context (single-page) and Multi Context (multi-page) scenarios, demanding robust cross-page reasoning.
 The benchmark assesses models using a specialized level-aware scoring mechanism to account for the varying complexities of L1 through L4 tasks. The inclusion of explicit reasoning ("Thinking") models reveals a significant paradigm shift in how MLLMs approach visual text reconstruction.
+![Model Benchmark Comparison](./png/model_bench.png)
 ### Main Results