Update README.md

README.md

@@ -44,32 +44,22 @@ Building upon previous DocVQA benchmarks, this evaluation dataset introduces cha
 
 By expanding coverage to new document domains and introducing richer question types, this benchmark seeks to push the boundaries of multimodal reasoning and promote the development of more general, robust document understanding models.
 
-# Datasets
+## 🏆 Competition Hosting & Datasets
 
-This dataset card corresponds to the **DocVQA 2026** benchmark used in the **ICDAR 2026 competition on multimodal reasoning over documents in multiple domains**.
+The official DocVQA 2026 competition is hosted on the **Robust Reading Competition (RRC)** platform, which provides the standardized framework for our leaderboards, submissions, and result tracking.
 
-The benchmark includes:
-
-- **Validation set** — contains public answers and is intended for local development and experimentation.
-- **Test set** — contains private answers and is used for the official competition ranking.
-
-The official competition is hosted on the **Robust Reading Competition (RRC)** platform: https://rrc.cvc.uab.es/?ch=34
-
-Participants interested in the leaderboard and official submissions should register and submit their predictions through the RRC server.
-
-### Validation Set
-
-The validation split includes public ground-truth answers and can be evaluated:
-
-- **Locally**, using the official evaluation code: https://github.com/VLR-CVC/DocVQA2026
-
-- **Online**, by submitting predictions to the RRC platform: https://rrc.cvc.uab.es/?ch=34&com=mymethods&task=1
+<p align="center">
+  <a href="https://rrc.cvc.uab.es/?ch=34" style="background-color: #007bff; color: white; padding: 12px 24px; text-decoration: none; border-radius: 6px; font-weight: bold; font-size: 18px; display: inline-block;">
+    Join the Challenge on the RRC Platform
+  </a>
+</p>
 
-### Test Set
+The benchmark includes:
 
-The test split contains **private answers** and therefore can only be evaluated through the official RRC platform: https://rrc.cvc.uab.es/?ch=34&com=mymethods&task=1
+- **Validation set** — contains public answers and is intended for local development and experimentation. It can be evaluated locally using the official evaluation code or online via the RRC platform.
+- **Test set** — contains **private answers** and is used for the official competition ranking. It can only be evaluated through the official RRC platform.
 
-# Participation Requirements
+## 📋 Participation Requirements
 
 To participate in the competition:
 
@@ -78,11 +68,11 @@ To participate in the competition:
 
 These reports will be included in the competition publication in the proceedings of the **International Conference on Document Analysis and Recognition (ICDAR)**, held in **Vienna, Austria**.
 
-# Competition Categories
+## 📊 Competition Categories
 
 There are **three participation categories**, depending on the total number of parameters of the submitted method.
 
-This count must include, all parameters whether active or not, and all parameters across all models used in agentic systems
+This count must include, all parameters whether active or not, and all parameters across all models used in agentic systems.
 
 Categories:
 
@@ -90,9 +80,7 @@ Categories:
 - **Over 8B parameters and up to 35B**
 - **Over 35B parameters**
 
----
-
-# Load & Inspect the Data
+## Load & Inspect the Data
 
 ```python
 from datasets import load_dataset
@@ -111,7 +99,7 @@ print(f"Document ID: {doc_id} ({category})")
 # 'document' is a list of PIL Images (one for each page)
 images = sample["document"]
 print(f"Number of pages: {len(images)}")
-images[0].show()
+images[0].show()  
 
 # 4. Access Questions and Answers
 questions = sample["questions"]
@@ -126,12 +114,11 @@ for q, q_id, a in zip(questions['question'], questions['question_id'], answers['
     print("-" * 50)
 ```
 
-
-# Structure of a Sample
+## Structure of a Sample
 
 <details>
 <summary><b>Click to expand the JSON structure</b></summary>
-
+  
 ```json
 {
   "doc_id": "maps_2",
@@ -173,13 +160,10 @@ for q, q_id, a in zip(questions['question'], questions['question_id'], answers['
     ]
   }
 }
-
 ```
 </details>
 
----
-
-# Results
+## Results
 
 <p align="center">
   <img src="./assets/results_chart.jpg" alt="DocVQA 2026 Results Chart" width="80%">
@@ -188,71 +172,135 @@ for q, q_id, a in zip(questions['question'], questions['question_id'], answers['
 </p>
 
 <div align="center">
+  <table>
+    <thead>
+      <tr>
+        <th align="left">Category</th>
+        <th align="center">Gemini 3 Pro Preview</th>
+        <th align="center">GPT-5.2</th>
+        <th align="center">Gemini 3 Flash Preview</th>
+        <th align="center">GPT-5 Mini</th>
+      </tr>
+    </thead>
+    <tbody>
+      <tr>
+        <td align="left"><b>Overall Accuracy</b></td>
+        <td align="center"><b>0.375</b></td>
+        <td align="center">0.350</td>
+        <td align="center">0.3375</td>
+        <td align="center">0.225</td>
+      </tr>
+      <tr>
+        <td align="left">Business Report</td>
+        <td align="center">0.400</td>
+        <td align="center"><b>0.600</b></td>
+        <td align="center">0.200</td>
+        <td align="center">0.300</td>
+      </tr>
+      <tr>
+        <td align="left">Comics</td>
+        <td align="center">0.300</td>
+        <td align="center">0.200</td>
+        <td align="center"><b>0.400</b></td>
+        <td align="center">0.100</td>
+      </tr>
+      <tr>
+        <td align="left">Engineering Drawing</td>
+        <td align="center">0.300</td>
+        <td align="center">0.300</td>
+        <td align="center"><b>0.500</b></td>
+        <td align="center">0.200</td>
+      </tr>
+      <tr>
+        <td align="left">Infographics</td>
+        <td align="center"><b>0.700</b></td>
+        <td align="center">0.600</td>
+        <td align="center">0.500</td>
+        <td align="center">0.500</td>
+      </tr>
+      <tr>
+        <td align="left">Maps</td>
+        <td align="center">0.000</td>
+        <td align="center"><b>0.200</b></td>
+        <td align="center">0.000</td>
+        <td align="center">0.100</td>
+      </tr>
+      <tr>
+        <td align="left">Science Paper</td>
+        <td align="center">0.300</td>
+        <td align="center">0.400</td>
+        <td align="center"><b>0.500</b></td>
+        <td align="center">0.100</td>
+      </tr>
+      <tr>
+        <td align="left">Science Poster</td>
+        <td align="center"><b>0.300</b></td>
+        <td align="center">0.000</td>
+        <td align="center">0.200</td>
+        <td align="center">0.000</td>
+      </tr>
+      <tr>
+        <td align="left">Slide</td>
+        <td align="center"><b>0.700</b></td>
+        <td align="center">0.500</td>
+        <td align="center">0.400</td>
+        <td align="center">0.500</td>
+      </tr>
+    </tbody>
+  </table>
+</div>
 
-| Category | Gemini 3 Pro Preview | GPT-5.2 | Gemini 3 Flash Preview | GPT-5 Mini |
-|---|---|---|---|---|
-| **Overall Accuracy** | **0.375** | 0.350 | 0.3375 | 0.225 |
-| Business Report | 0.400 | **0.600** | 0.200 | 0.300 |
-| Comics | 0.300 | 0.200 | **0.400** | 0.100 |
-| Engineering Drawing | 0.300 | 0.300 | **0.500** | 0.200 |
-| Infographics | **0.700** | 0.600 | 0.500 | 0.500 |
-| Maps | 0.000 | **0.200** | 0.000 | 0.100 |
-| Science Paper | 0.300 | 0.400 | **0.500** | 0.100 |
-| Science Poster | **0.300** | 0.000 | 0.200 | 0.000 |
-| Slide | **0.700** | 0.500 | 0.400 | 0.500 |
+> [!NOTE]
+> **Evaluation Parameters:**
+> * **GPT Models:** "High thinking" enabled, temperature set to `1.0`.
+> * **Gemini Models:** "High thinking" enabled, temperature set to `1.0`.
 
-</div>
+> [!WARNING]
+> **API Constraints:** Both models were evaluated via their respective APIs. If a sample fails because the input files are too large, the result counts as a failure. For example, the file input limit for OpenAI models is 50MB, and several comics in this dataset surpass that threshold.
 
-> **Evaluation Parameters**
->
-> - GPT models: "High thinking" enabled, temperature = 1.0  
-> - Gemini models: "High thinking" enabled, temperature = 0.0
+--------
 
-> **API Constraints**
->
-> Both model families were evaluated via their respective APIs. If a sample fails because the input files are too large, the result counts as a failure.  
-> For example, several comics exceed the 50MB file input limit present in some API models.
+## 📝 Submission Guidelines & Formatting Rules
 
----
+To ensure fair and accurate evaluation across all participants, submissions are evaluated using automated metrics. Therefore, all model outputs must strictly adhere to the following formatting rules:
+
+* **Source Adherence:** Only provide answers found directly within the document. If the question is unanswerable given the provided image, the response must be exactly: `"Unknown"`.
+* **Multiple Answers:** List multiple answers in their order of appearance, separated by a comma and a single space. **Do not** use the word "and". *(Example: `Answer A, Answer B`)*
+* **Numbers & Units:** Convert units to their standardized abbreviations (e.g., use `kg` instead of "kilograms", `m` instead of "meters"). Always place a single space between the number and the unit. *(Example: `50 kg`, `10 USD`)*
+* **Percentages:** Attach the `%` symbol directly to the number with no space. *(Example: `50%`)*
+* **Dates:** Convert all dates to the standardized `YYYY-MM-DD` format. *(Example: "Jan 1st 24" becomes `2024-01-01`)*
+* **Decimals:** Use a single period (`.`) as a decimal separator, never a comma. *(Example: `3.14`)*
+* **Thousands Separator:** Do not use commas to separate large numbers. *(Example: `1000`, not `1,000`)*
+* **No Filler Text:** Output **only** the requested data. Do not frame your answer in full sentences (e.g., avoid "The answer is...").
 
-# Answer Formatting Rules
-- **Source Adherence**  
-  Only provide answers found directly within the document.  
-  If the question is unanswerable given the provided image, the response must be exactly:  
-  `Unknown`
+**Final Output Format:** When generating the final extracted data, your system must prefix the response with the following exact phrasing:
 
-- **Multiple Answers**  
-  List multiple answers in their order of appearance, separated by a comma and a single space.  
-  Do not use the word "and".  
-  Example: `Answer A, Answer B`
+```text
+FINAL ANSWER: [Your formatted answer]
+```
 
-- **Numbers & Units**  
-  Convert units to standardized abbreviations (`kg`, `m`, etc.).  
-  Always place a single space between number and unit.  
-  Example: `50 kg`, `10 USD`
+---------
+## Evaluation Code & Baselines
 
-- **Percentages**  
-  Attach `%` directly to the number with no space.  
-  Example: `50%`
+To ensure consistency and fairness, all submissions are evaluated using our official automated evaluation pipeline. This pipeline handles the extraction of your model's answers and applies both strict formatting checks (for numbers, dates, and units) and relaxed text matching (ANLS) for text-based answers.
 
-- **Dates**  
-  Convert dates to `YYYY-MM-DD`.  
-  Example: `Jan 1st 24` → `2024-01-01`
+You can find the complete, ready-to-use evaluation script in our official GitHub repository:
+🖥️ **[VLR-CVC/DocVQA2026 GitHub Repository](https://github.com/VLR-CVC/DocVQA2026)**
 
-- **Decimals**  
-  Use a period `.` as decimal separator.  
-  Example: `3.14`
+### What you will find in the repository:
 
-- **Thousands Separator**  
-  Do not use commas.  
-  Example: `1000`
+* **The Evaluator Script:** The core logic used to parse your model's outputs and calculate the final scores. You can use this script to test and evaluate your predictions locally before making an official submission.
+* **The Baseline Master Prompt:** We have included the exact prompt structure (`get_evaluation_prompt()`) used for our baseline experiments. This prompt is heavily engineered to enforce the competition's mandatory reasoning protocols and strict output formatting. 
 
-- **No Filler Text**  
-  Output only the requested data.
+We highly recommend reviewing both the evaluation script and the Master Prompt. You are welcome to use the provided prompt out-of-the-box or adapt it to better guide your own custom models!
 
----
+## Dataset Structure
+
+The dataset consists of:
+1.  **Images:** High-resolution PNG renders of document pages located in the `images/` directory.
+2.  **Annotations:** A Parquet file (`val.parquet`) containing the questions, answers, and references to the image paths.
 
-# Contact
+## Contact
 
 For questions, technical support, or inquiries regarding the DocVQA 2026 dataset and competition framework: **docvqa@cvc.uab.cat**