LUC-XAVIER/CamDocLM

A fine-tuned model that recognizes and extracts text from NICs and passports in Cameroon # CamDocLM Fine-tuning LayoutLM on synthetic Cameroon NICs and passports generated with SynthText. ## Overview This project combines SynthText for synthetic dataset generation with LayoutLM for document understanding. The goal is to build a model that can process and classify structured identity documents (NICs, passports). ## Environment Setup Create a Conda environment with Python 3.11: ```bash conda create -n camdoclm_env python=3.11 -y conda activate camdoclm_env pip install torch torchvision torchaudio pip install transformers datasets huggingface_hub pip install opencv-python pillow numpy matplotlib scipy shapely tqdm ``` ## Project Structure: ``` CamDocLM/ │── data/ # Generated NICs & passports │── configs/ # SynthText + training configs │── scripts/ # Preprocessing & training scripts │── notebooks/ # Experiment notebooks │── external/ │ └── SynthText/ # Cloned SynthText repo (ignored in git) │── README.md │── .gitignore ``` ## Workflow Clone this repo: ``` git clone github.com cd CamDocLM ``` Clone SynthText inside SynthText_Service (not tracked by git): ``` mkdir SynthText_Service cd SynthText_Service git clone github.com cd .. ```