1. Submission Requirements: Participants must provide the trained model and the testing script to allow for reproduction of results.

2. Code and Model Ownership: The submitted code and model will be owned by the organizers. 

3. Evaluation Results: Validation and test phase results will be shared with participants via email; there will be no public leaderboard or online result checking.

4. Transparency: These guidelines help ensure a fair and consistent evaluation process for all participants.

Each participating team must submit their solutions in the following format. Each team is required to email their solution in a zipped file to ncvpripg2025dehadoai@gmail.com with the following specifications:

1. The final submission should include:  

• The Python inference script  

• The trained model file(s)  

• Any required utility/helper scripts

• A clear README with instructions for running the code

• Naming format: TeamName_DeHaDo-AI_Challenge.zip 

• Submission through the designated portal (to be announced). 

2. Recognition Output Format – The text extracted from handwritten forms should be converted into a structured JSON format, including both the recognized text and corresponding coordinates. Each image should have an individual JSON output file.

3. Model Architecture & Code – AI model implementation, including training scripts and inference pipeline. Teams retain ownership of their code, and evaluation results will only be used with explicit consent. 

4. Technical Report – A document outlining the approach, methodology, and performance analysis.

5. Evaluation Metrics Report – Summary of text recognition accuracy, field validation performance, and computational efficiency.

6. Executable Demo (Optional) – A working prototype or API demonstrating real-time handwritten form processing

*Note on the model/Folder

We have provided a model/ folder containing sample trained model files to help participants test their inference pipeline. You can use any one of the provided models for initial testing—this is sufficient to verify your code structure and functionality.

During final submission, you must replace the sample model with your own trained model, ensuring that the code can reproduce your submitted results using this model.

Sample Folder Structure for Submission:

submission/

│

├── model/

│   └── handwritten_model.pth           # Sample trained model (PyTorch format)

│   └── handwritten_model.h5            # OR TensorFlow/Keras format (if applicable)

│   └── handwritten_model.onnx        # Optional: Exported ONNX model

│

├── src/

│   ├── inference.py                       # Main Python script for running inference

│   ├── utils.py                                    # Helper functions (e.g., image preprocessing)

│   └── model_architecture.py            # Model architecture definition

│

├── data/

│   └── sample_input/                         # Optional: sample test data

│

├── README.md                                # Instructions to reproduce the results

└── requirements.txt                           # Python dependencies

• Participants may use the provided fact sheet template to submit their result during the final phase of the competition.