--- license: cc-by-nc-4.0 language: - en - ar - hi tags: - Document_Understanding - Document_Packet_Splitting - Document_Comprehension - Document_Classification - Document_Recognition - Document_Segmentation pretty_name: DocSplit Benchmark size_categories: - 1M **Note:** The `image_path` and `text_path` fields in each page reference assets that are not included in the dataset download. See [Data Formats](#data-formats) for details. # DocSplit: Document Packet Splitting Benchmark Generator A toolkit for creating benchmark datasets to test document packet splitting systems. Document packet splitting is the task of separating concatenated multi-page documents into individual documents with correct page ordering. ## Overview This toolkit generates five benchmark datasets of varying complexity to test how well models can: 1. **Detect document boundaries** within concatenated packets 2. **Classify document types** accurately 3. **Reconstruct correct page ordering** within each document ## Dataset Schema When loaded via `load_dataset()`, each row contains: | Field | Type | Description | |-------|------|-------------| | `doc_id` | string | UUID identifying the spliced packet | | `total_pages` | int | Total number of pages in the packet | | `subdocuments` | list | Array of constituent documents | Each subdocument contains: | Field | Type | Description | |-------|------|-------------| | `doc_type_id` | string | Document type category | | `local_doc_id` | string | Identifier within the packet | | `group_id` | string | Group identifier | | `page_ordinals` | list[int] | Page positions within the packet | | `pages` | list | Per-page metadata (image_path, text_path, original_doc_name) | ## Document Source We uses the documents from **RVL-CDIP-N-MP**: [https://huggingface.co/datasets/jordyvl/rvl_cdip_n_mp](https://huggingface.co/datasets/jordyvl/rvl_cdip_n_mp) ## Quick Start ### Clone from Hugging Face This repository is hosted on Hugging Face at: [https://huggingface.co/datasets/amazon/doc_split](https://huggingface.co/datasets/amazon/doc_split) Choose one of the following methods to download the repository: #### Option 1: Using Git with Git LFS (Recommended) Git LFS (Large File Storage) is required for Hugging Face datasets as they often contain large files. **Install Git LFS:** ```bash # Linux (Ubuntu/Debian): sudo apt-get install git-lfs git lfs install # macOS (Homebrew): brew install git-lfs git lfs install # Windows: Download from https://git-lfs.github.com, then run: # git lfs install ``` **Clone the repository:** ```bash git clone https://huggingface.co/datasets/amazon/doc_split cd doc_split pip install -r requirements.txt ``` #### Option 2: Using Hugging Face CLI ```bash # 1. Install the Hugging Face Hub CLI pip install -U "huggingface_hub[cli]" # 2. (Optional) Login if authentication is required huggingface-cli login # 3. Download the dataset huggingface-cli download amazon/doc_split --repo-type dataset --local-dir doc_split # 4. Navigate and install dependencies cd doc_split pip install -r requirements.txt ``` #### Option 3: Using Python SDK (huggingface_hub) ```python from huggingface_hub import snapshot_download # Download the entire dataset repository local_dir = snapshot_download( repo_id="amazon/doc_split", repo_type="dataset", local_dir="doc_split" ) print(f"Dataset downloaded to: {local_dir}") ``` Then install dependencies: ```bash cd doc_split pip install -r requirements.txt ``` #### Tips - **Check Disk Space**: Hugging Face datasets can be large. Check the "Files and versions" tab on the Hugging Face page to see the total size before downloading. - **Partial Clone**: If you only need specific files (e.g., code without large data files), use: ```bash GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/amazon/doc_split cd doc_split # Then selectively pull specific files: git lfs pull --include="*.py" ``` --- ## Usage ### Step 1: Create Assets Convert raw PDFs into structured assets with page images (300 DPI PNG) and OCR text (Markdown). > **Note:** The code defaults for `--raw-data-path` (`../raw_data`) and `--output-path` (`../processed_assets`) assume running from within `src/assets/`. When running from the repo root, pass explicit paths as shown below. #### Option A: AWS Textract OCR (Default) > **⚠️ Requires Python 3.12:** This command uses `amazon-textract-textractor`, which has C extension dependencies that may not build on Python 3.13+. See [Requirements](#requirements). Best for English documents. Processes all document categories with Textract. ```bash python src/assets/run.py \ --raw-data-path data/raw_data \ --output-path data/assets \ --s3-bucket your-bucket-name \ --s3-prefix textract-temp \ --workers 10 \ --save-mapping ``` **Requirements:** - AWS credentials configured (`aws configure`) - S3 bucket for temporary file uploads - No GPU required #### Option B: Hybrid OCR (Textract + DeepSeek) Uses Textract for most categories, DeepSeek OCR only for the "language" category (multilingual documents). **Note:** For this project, DeepSeek OCR was used only for the "language" category and executed in AWS SageMaker AI with GPU instances (e.g., `ml.g6.xlarge`). **1. Install flash-attention (Required for DeepSeek):** ```bash # For CUDA 12.x with Python 3.12: cd /mnt/sagemaker-nvme # Use larger disk for downloads wget https://github.com/Dao-AILab/flash-attention/releases/download/v2.8.3/flash_attn-2.8.3+cu12torch2.9cxx11abiTRUE-cp312-cp312-linux_x86_64.whl pip install flash_attn-2.8.3+cu12torch2.9cxx11abiTRUE-cp312-cp312-linux_x86_64.whl # For other CUDA/Python versions: https://github.com/Dao-AILab/flash-attention/releases ``` **2. Set cache directory (Important for SageMaker):** ```bash # SageMaker: Use larger NVMe disk instead of small home directory export HF_HOME=/mnt/sagemaker-nvme/cache export TRANSFORMERS_CACHE=/mnt/sagemaker-nvme/cache ``` **3. Run asset creation:** ```bash python src/assets/run.py \ --raw-data-path data/raw_data \ --output-path data/assets \ --s3-bucket your-bucket-name \ --use-deepseek-for-language \ --workers 10 \ --save-mapping ``` **Requirements:** - NVIDIA GPU with CUDA support (tested on ml.g6.xlarge) - ~10GB+ disk space for model downloads - flash-attention library installed - AWS credentials (for Textract on non-language categories) - S3 bucket (for Textract on non-language categories) **How it works:** - Documents in `raw_data/language/` → DeepSeek OCR (GPU) - All other categories → AWS Textract (cloud) #### Parameters - `--raw-data-path`: Directory containing source PDFs organized by document type - `--output-path`: Where to save extracted assets (images + OCR text) - `--s3-bucket`: S3 bucket name (required for Textract) - `--s3-prefix`: S3 prefix for temporary files (default: textract-temp) - `--workers`: Number of parallel processes (default: 10) - `--save-mapping`: Save CSV mapping document IDs to file paths - `--use-deepseek-for-language`: Use DeepSeek OCR for "language" category only - `--limit`: Process only N documents (useful for testing) #### What Happens 1. Scans `raw_data/` directory for PDFs organized by document type 2. Extracts each page as 300 DPI PNG image 3. Runs OCR (Textract or DeepSeek) to extract text 4. Saves structured assets in `output-path/{doc_type}/{doc_name}/` 5. Optionally creates `document_mapping.csv` listing all processed documents 6. These assets become the input for Step 2 (benchmark generation) #### Output Structure ``` data/assets/ └── {doc_type}/{filename}/ ├── original/{filename}.pdf └── pages/{page_num}/ ├── page-{num}.png # 300 DPI image └── page-{num}-textract.md # OCR text ``` ## Interactive Notebooks Explore the toolkit with Jupyter notebooks: 1. **`notebooks/01_create_assets.ipynb`** - Create assets from PDFs 2. **`notebooks/02_create_benchmarks.ipynb`** - Generate benchmarks with different strategies 3. **`notebooks/03_analyze_benchmarks.ipynb`** - Analyze and visualize benchmark statistics ## Data Formats The dataset provides two complementary formats for each benchmark: ### Ground Truth JSON (used by `load_dataset`) One JSON file per document packet in `datasets/{strategy}/{size}/ground_truth_json/{split}/`: ```json { "doc_id": "...", "total_pages": ..., "subdocuments": [ { "doc_type_id": "...", "local_doc_id": "...", "group_id": "...", "page_ordinals": [...], "pages": [ { "page": 1, "original_doc_name": "...", "image_path": "rvl-cdip-nmp-assets/...", "text_path": "rvl-cdip-nmp-assets/...", "local_doc_id_page_ordinal": ... } ] } ] } ``` ### CSV (flat row-per-page format) One CSV per split in `datasets/{strategy}/{size}/`: | Column | Description | |--------|-------------| | `doc_type` | Document type category | | `original_doc_name` | Source document filename | | `parent_doc_name` | UUID of the spliced packet (matches `doc_id` in JSON) | | `local_doc_id` | Local identifier within the packet | | `page` | Page number within the packet | | `image_path` | Path to page image (prefix: `data/assets/`) | | `text_path` | Path to OCR text (prefix: `data/assets/`) | | `group_id` | Group identifier | | `local_doc_id_page_ordinal` | Page ordinal within the original source document | ### Asset Paths The image and text paths in both formats reference assets that are **not included** in this repository: - JSON paths use prefix `rvl-cdip-nmp-assets/` - CSV paths use prefix `data/assets/` To resolve these paths, run the asset creation pipeline (see [Create Assets](#step-1-create-assets)). The data can be used for metadata and label analysis without the actual images. ## Requirements - Python 3.12+ recommended (see note below) - AWS credentials (for Textract OCR) - Dependencies: `pip install -r requirements.txt` > **⚠️ Python Version:** The `amazon-textract-textractor` package (required by `src/assets/run.py`) depends on C extensions (`editdistance`) that may fail to build on Python 3.13+. **Python 3.12 is recommended.** Using [uv](https://docs.astral.sh/uv/) as your package installer can also help resolve build issues. > **Note:** `requirements.txt` currently includes GPU dependencies (PyTorch, Transformers) that are only needed for DeepSeek OCR on multilingual documents. If you only need Textract OCR or want to explore the pre-generated data, the core dependencies are: `boto3`, `loguru`, `pymupdf`, `pillow`, `pydantic`, `amazon-textract-textractor`, `tenacity`. --- ### Download Source Data and Generate Benchmarks ```bash # 1. Download and extract RVL-CDIP-N-MP source data from HuggingFace (1.25 GB) # This dataset contains multi-page PDFs organized by document type # (invoices, letters, forms, reports, etc.) mkdir -p data/raw_data cd data/raw_data wget https://huggingface.co/datasets/jordyvl/rvl_cdip_n_mp/resolve/main/data.tar.gz tar -xzf data.tar.gz rm data.tar.gz cd ../.. # 2. Create assets from raw PDFs # Extracts each page as PNG image and runs OCR to get text # These assets are then used in step 3 to create benchmark datasets # Output: Structured assets in data/assets/ with images and text per page python src/assets/run.py --raw-data-path data/raw_data --output-path data/assets # 3. Generate benchmark datasets # This concatenates documents using different strategies and creates # train/test/validation splits with ground truth labels # Output: Benchmark JSON files in data/benchmarks/ ready for model evaluation python src/benchmarks/run.py \ --strategy poly_seq \ --assets-path data/assets \ --output-path data/benchmarks ``` ## Pipeline Overview ``` Raw PDFs → [Create Assets] → Page Images + OCR Text → [Generate Benchmarks] → DocSplit Benchmarks ``` ## Five Benchmark Datasets The toolkit generates five benchmarks of increasing complexity, based on the DocSplit paper: ### 1. **DocSplit-Mono-Seq** (`mono_seq`) **Single Category Document Concatenation Sequentially** - Concatenates documents from the same category - Preserves original page order - **Challenge**: Boundary detection without category transitions as discriminative signals - **Use Case**: Legal document processing where multiple contracts of the same type are bundled ### 2. **DocSplit-Mono-Rand** (`mono_rand`) **Single Category Document Pages Randomization** - Same as Mono-Seq but shuffles pages within documents - **Challenge**: Boundary detection + page sequence reconstruction - **Use Case**: Manual document assembly with page-level disruptions ### 3. **DocSplit-Poly-Seq** (`poly_seq`) **Multi Category Documents Concatenation Sequentially** - Concatenates documents from different categories - Preserves page ordering - **Challenge**: Inter-document boundary detection with category diversity - **Use Case**: Medical claims processing with heterogeneous documents ### 4. **DocSplit-Poly-Int** (`poly_int`) **Multi Category Document Pages Interleaving** - Interleaves pages from different categories in round-robin fashion - **Challenge**: Identifying which non-contiguous pages belong together - **Use Case**: Mortgage processing where deeds, tax records, and notices are interspersed ### 5. **DocSplit-Poly-Rand** (`poly_rand`) **Multi Category Document Pages Randomization** - Complete randomization across all pages (maximum entropy) - **Challenge**: Worst-case scenario with no structural assumptions - **Use Case**: Document management system failures or emergency recovery ### Dataset Statistics The pre-generated benchmarks include train, test, and validation splits in both `small` (5–20 pages per packet) and `large` (20–500 pages per packet) sizes. For `mono_rand/large`: | Split | Document Count | |-------|---------------| | Train | 417 | | Test | 96 | | Validation | 51 | ## Project Structure ``` doc-split-benchmark/ ├── README.md ├── requirements.txt ├── src/ │ ├── assets/ # Asset creation from PDFs │ │ ├── __init__.py │ │ ├── models.py │ │ ├── run.py # Main entry point │ │ └── services/ │ │ ├── __init__.py │ │ ├── asset_creator.py │ │ ├── asset_writer.py │ │ ├── deepseek_ocr.py │ │ ├── pdf_loader.py │ │ └── textract_ocr.py │ │ │ └── benchmarks/ # Benchmark generation │ ├── __init__.py │ ├── models.py │ ├── run.py # Main entry point │ └── services/ │ ├── __init__.py │ ├── asset_loader.py │ ├── split_manager.py │ ├── benchmark_generator.py │ ├── benchmark_writer.py │ └── shuffle_strategies/ │ ├── __init__.py │ ├── base_strategy.py │ ├── mono_seq.py │ ├── mono_rand.py │ ├── poly_seq.py │ ├── poly_int.py │ └── poly_rand.py │ ├── notebooks/ │ ├── 01_create_assets.ipynb │ ├── 02_create_benchmarks.ipynb │ └── 03_analyze_benchmarks.ipynb │ ├── datasets/ # Pre-generated benchmark data │ └── {strategy}/{size}/ │ ├── train.csv │ ├── test.csv │ ├── validation.csv │ └── ground_truth_json/ │ ├── train/*.json │ ├── test/*.json │ └── validation/*.json │ └── data/ # Generated by toolkit (not in repo) ├── raw_data/ ├── assets/ └── benchmarks/ ``` ### Generate Benchmarks [Detailed] Create DocSplit benchmarks with train/test/validation splits. ```bash python src/benchmarks/run.py \ --strategy poly_seq \ --assets-path data/assets \ --output-path data/benchmarks \ --num-docs-train 800 \ --num-docs-test 500 \ --num-docs-val 200 \ --size small \ --random-seed 42 ``` **Parameters:** - `--strategy`: Benchmark strategy - `mono_seq`, `mono_rand`, `poly_seq`, `poly_int`, `poly_rand`, or `all` (default: all) - `--assets-path`: Directory containing assets from Step 1 (default: data/assets) - `--output-path`: Where to save benchmarks (default: data/benchmarks) - `--num-docs-train`: Number of spliced documents for training (default: 800) - `--num-docs-test`: Number of spliced documents for testing (default: 500) - `--num-docs-val`: Number of spliced documents for validation (default: 200) - `--size`: Benchmark size - `small` (5-20 pages) or `large` (20-500 pages) (default: small) - `--split-mapping`: Path to split mapping JSON (default: data/metadata/split_mapping.json) - `--random-seed`: Seed for reproducibility (default: 42) **What Happens:** 1. Loads all document assets from Step 1 2. Creates or loads stratified train/test/val split (60/25/15 ratio) 3. Generates spliced documents by concatenating/shuffling pages per strategy 4. Saves benchmark CSV files with ground truth labels **Output Structure:** ``` data/ ├── metadata/ │ └── split_mapping.json # Document split assignments (shared across strategies) └── benchmarks/ └── {strategy}/ # e.g., poly_seq, mono_rand └── {size}/ # small or large ├── train.csv ├── test.csv └── validation.csv ``` # How to cite this dataset ```bibtex @misc{islam2026docsplitcomprehensivebenchmarkdataset, title={DocSplit: A Comprehensive Benchmark Dataset and Evaluation Approach for Document Packet Recognition and Splitting}, author={Md Mofijul Islam and Md Sirajus Salekin and Nivedha Balakrishnan and Vincil C. Bishop III and Niharika Jain and Spencer Romo and Bob Strahan and Boyi Xie and Diego A. Socolinsky}, year={2026}, eprint={2602.15958}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2602.15958}, } ``` # License Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. SPDX-License-Identifier: CC-BY-NC-4.0