```
├── .github/
├── workflows/
├── ci.yml (200 tokens)
├── .gitignore
├── .pre-commit-config.yaml
├── README.md (700 tokens)
├── assets/
├── input.png
├── output.png
├── pipeline.png
├── commonforms/
├── __init__.py
├── __main__.py (400 tokens)
├── exceptions.py
├── form_creator.py (1300 tokens)
├── inference.py (1400 tokens)
├── utils.py (100 tokens)
├── dataset/
├── generate_coco.py (1200 tokens)
├── merge_coco.py (700 tokens)
├── split_dataset.py (900 tokens)
├── test.csv (7.9k tokens)
├── val.csv (4.8k tokens)
├── pyproject.toml (100 tokens)
├── tests/
├── inference_test.py (300 tokens)
├── resources/
├── encrypted.pdf
├── input.pdf
```
## /.github/workflows/ci.yml
```yml path="/.github/workflows/ci.yml"
name: CI (uv + pytest)
on:
push:
branches: [ "**" ]
pull_request:
jobs:
test:
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
python-version: ["3.10", "3.11", "3.12", "3.13"]
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- name: Set up uv
uses: astral-sh/setup-uv@v3
- name: Cache uv and .venv
uses: actions/cache@v4
with:
path: |
~/.cache/uv
.venv
key: uv-${{ runner.os }}-${{ matrix.python-version }}-${{ hashFiles('pyproject.toml', 'uv.lock') }}
restore-keys: |
uv-${{ runner.os }}-${{ matrix.python-version }}-
- name: Install (uv)
run: |
uv sync --dev
- name: Run tests (pytest)
run: |
uv run -m pytest
```
## /.gitignore
```gitignore path="/.gitignore"
.python-version
.DS_Store
*.pyc
__pycache__
*.egg-info
uv.lock
*.pdf
dist
models/
```
## /.pre-commit-config.yaml
```yaml path="/.pre-commit-config.yaml"
repos:
- repo: https://github.com/astral-sh/ruff-pre-commit
# Ruff version.
rev: v0.13.0
hooks:
# Run the linter.
- id: ruff-check
args: [ --fix ]
# Run the formatter.
- id: ruff-format
```
## /README.md
# CommonForms
🪄 Automatically convert a PDF into a fillable form.
[💻 Hosted Models (detect.semanticdocs.org)](https://detect.semanticdocs.org) | [📄 CommonForms Paper](https://arxiv.org/abs/2509.16506) | [🤗 Dataset](https://huggingface.co/datasets/jbarrow/CommonForms) | [🤗 FFDNet-L](https://huggingface.co/jbarrow/FFDNet-L) | [🤗 FFDNet-S](https://huggingface.co/jbarrow/FFDNet-S)
This repo contains three things:
1. the pip-installable `commonforms` package, which has a CLI and API for converting PDFs into fillable forms
2. the FFDNet-S and FFDNet-L models from the paper [CommonForms: A Large, Diverse Dataset for Form Field Detection](https://arxiv.org/abs/2509.16506)
3. the preprocessing code for the CommonForms dataset, which is hosted on HuggingFace: https://huggingface.co/datasets/jbarrow/CommonForms
## Installation
CommonForms can be installed with either `uv` or `pip`, feel free to choose your package manager flavor:
```sh
uv pip install commonforms
```
Once it's installed, you should be able to run the CLI command on ~any PDF.
## CommonForms CLI
The simplest usage will run inference on your CPU using the default suggested settings:
```
commonforms <input.pdf> <output.pdf>
```
| Input | Output |
|-------|--------|
|  |  |
### Command Line Arguments
| Argument | Type | Default | Description |
|----------|------|---------|-------------|
| `input` | Path | Required | Path to the input PDF file |
| `output` | Path | Required | Path to save the output PDF file |
| `--model` | str | `FFDNet-L` | Model name (FFDNet-L/FFDNet-S) or path to custom .pt file |
| `--keep-existing-fields` | flag | `False` | Keep existing form fields in the PDF |
| `--use-signature-fields` | flag | `False` | Use signature fields instead of text fields for detected signatures |
| `--device` | str | `cpu` | Device for inference (e.g., `cpu`, `cuda`, `0`) |
| `--image-size` | int | `1600` | Image size for inference |
| `--confidence` | float | `0.3` | Confidence threshold for detection |
| `--fast` | flag | `False` | If running on a CPU, you can trade off accuracy for speed and run in about half the time |
| `--multiline` | flag | `False` | If you want the detected textboxes to allow multiline inputs |
## CommonForms API
In addition to the CLI, you can use
```py
from commonforms import prepare_form
prepare_form(
"path/to/input.pdf",
"path/to/output.pdf"
)
```
All of the above arguments are keyword arguments to the `prepare_form` function.
## Dataset Prep
🚧 Code for dataset prep exists in the `dataset` folder.
# Citation
If you use the tool, models, or code in an academic paper, please cite the CommonForms paper:
```
@misc{barrow2025commonforms,
title = {CommonForms: A Large, Diverse Dataset for Form Field Detection},
author = {Barrow, Joe},
year = {2025},
eprint = {2509.16506},
archivePrefix= {arXiv},
primaryClass = {cs.CV},
doi = {10.48550/arXiv.2509.16506},
url = {https://arxiv.org/abs/2509.16506}
}
```
If you use it in a non-academic setting, please reach out to the author (joseph.d.barrow [at] gmail.com)!
I love to hear when people are using my work!
## /assets/input.png
Binary file available at https://raw.githubusercontent.com/jbarrow/commonforms/refs/heads/main/assets/input.png
## /assets/output.png
Binary file available at https://raw.githubusercontent.com/jbarrow/commonforms/refs/heads/main/assets/output.png
## /assets/pipeline.png
Binary file available at https://raw.githubusercontent.com/jbarrow/commonforms/refs/heads/main/assets/pipeline.png
## /commonforms/__init__.py
```py path="/commonforms/__init__.py"
from commonforms.inference import prepare_form
def main():
from commonforms.__main__ import main as cli_main
cli_main()
__all__ = ["prepare_form", "main"]
```
## /commonforms/__main__.py
```py path="/commonforms/__main__.py"
from commonforms.inference import prepare_form
from argparse import ArgumentParser
from pathlib import Path
def main():
parser = ArgumentParser(
prog="commonforms", description="Automatically Prepare a Fillable PDF Form"
)
parser.add_argument(
"input",
type=Path,
help="Path to the input file (only .pdf files are supported for now.)",
)
parser.add_argument("output", type=Path, help="Path to save the output PDF file.")
parser.add_argument(
"--model",
type=str,
default="FFDNet-L",
help="Model (FFDNet-L/FFDNet-S) or path to a different .pt model",
)
parser.add_argument(
"--keep-existing-fields",
action="store_true",
help="If true, keep existing form fields on the PDF",
)
parser.add_argument(
"--use-signature-fields",
action="store_true",
help="If true, use signature fields instead of text fields for detected signatures",
)
parser.add_argument(
"--device", default="cpu", help="Which device to use for inference."
)
parser.add_argument(
"--image-size",
type=int,
default=1600,
dest="image_size",
help="Image size for inference (default: 1600)",
)
parser.add_argument(
"--confidence",
type=float,
default=0.3,
help="Confidence threshold for detection (default: 0.3)",
)
parser.add_argument(
"--fast",
action="store_true",
help="If running on a CPU, you can use --fast to get a 50% speedup with a small accuracy penalty",
)
parser.add_argument(
"--multiline",
action="store_true",
help="If you want the detected textboxes to allow multiline inputs.",
)
args = parser.parse_args()
prepare_form(
args.input,
args.output,
model_or_path=args.model,
keep_existing_fields=args.keep_existing_fields,
use_signature_fields=args.use_signature_fields,
device=args.device,
image_size=args.image_size,
confidence=args.confidence,
fast=args.fast,
multiline=args.multiline,
)
if __name__ == "__main__":
main()
```
## /commonforms/exceptions.py
```py path="/commonforms/exceptions.py"
class EncryptedPdfError(Exception):
pass
```
## /commonforms/form_creator.py
```py path="/commonforms/form_creator.py"
from pypdf import PdfWriter, PdfReader
from pypdf.annotations import AnnotationDictionary
from pypdf.generic import (
NameObject,
ArrayObject,
NumberObject,
TextStringObject,
DictionaryObject,
)
from commonforms.utils import BoundingBox
def rect_for(bounding_box: BoundingBox, page) -> ArrayObject:
# because the PDFs are rendered to images with the CropBox, we need to use
# that as the offset for where we insert the widgets
page = page.cropbox if page.cropbox else page.mediabox
# here I'm flipping the page.top/page.bottom to change from top-left origin
# to bottom-right origin; this results in a negative height, but the math
# works out in the end
page_x0, page_y0, page_x1, page_y1 = (page.left, page.top, page.right, page.bottom)
page_width = page_x1 - page_x0
page_height = page_y1 - page_y0
x0 = page_x0 + (bounding_box.x0 * page_width)
y0 = page_y0 + (bounding_box.y1 * page_height)
x1 = page_x0 + (bounding_box.x1 * page_width)
y1 = page_y0 + (bounding_box.y0 * page_height)
if x0 > x1:
x0, x1 = x1, x0
if y0 > y1:
y0, y1 = y1, y0
return ArrayObject(
[
NumberObject(x0),
NumberObject(y0),
NumberObject(x1),
NumberObject(y1),
]
)
class Textbox(AnnotationDictionary):
def __init__(
self,
name: str,
rect: ArrayObject,
*,
multiline: bool = False,
value: str | None = None,
default_value: str | None = None,
):
super().__init__()
self.update(
{
NameObject("/Type"): NameObject("/Annot"),
NameObject("/Subtype"): NameObject("/Widget"),
NameObject("/FT"): NameObject("/Tx"),
NameObject("/T"): TextStringObject(name or ""),
NameObject("/V"): TextStringObject(value or ""),
NameObject("/DV"): TextStringObject(default_value or ""),
NameObject("/Ff"): NumberObject(0 if not multiline else (1 << 12)),
NameObject("/Rect"): rect,
NameObject("/DA"): TextStringObject("/Helv 0 Tf 0 0 0 rg"),
}
)
class Checkbox(AnnotationDictionary):
def __init__(
self,
name: str,
rect: ArrayObject,
*,
multiline: bool = False,
value: bool | None = None,
default_value: str | None = None,
):
super().__init__()
pdf_value = NameObject("/Off") if not value else NameObject("/Yes")
self.update(
{
NameObject("/Type"): NameObject("/Annot"),
NameObject("/Subtype"): NameObject("/Widget"),
NameObject("/FT"): NameObject("/Btn"),
NameObject("/Ff"): NumberObject(0),
NameObject("/Rect"): rect,
NameObject("/V"): pdf_value,
NameObject("/AS"): pdf_value,
NameObject("/T"): TextStringObject(name),
}
)
class Signature(AnnotationDictionary):
def __init__(self, name: str, rect: ArrayObject):
super().__init__()
self.update(
{
NameObject("/Type"): NameObject("/Annot"),
NameObject("/Subtype"): NameObject("/Widget"),
NameObject("/FT"): NameObject("/Sig"),
NameObject("/T"): TextStringObject(name),
NameObject("/Rect"): rect,
NameObject("/F"): NumberObject(4),
}
)
class PyPdfFormCreator:
def __init__(self, input_path: str):
self.reader = PdfReader(input_path)
# NOTE: Commenting out add_form_topname as it causes lazy loading issues with pages
# self.reader.add_form_topname("original")
self.writer = PdfWriter(clone_from=self.reader)
# Keep reader open until we're done - pypdf uses lazy loading
zapf_font = DictionaryObject(
{
NameObject("/Type"): NameObject("/Font"),
NameObject("/Subtype"): NameObject("/Type1"),
NameObject("/BaseFont"): NameObject("/ZapfDingbats"),
NameObject("/Name"): NameObject("/ZaDb"),
}
)
self.zapf_font = self.writer._add_object(zapf_font)
def clear_existing_fields(self):
"""Clear all existing form fields from the PDF."""
# Get the root form object if it exists
if hasattr(self.writer, "_root_object"):
root = self.writer._root_object
if NameObject("/AcroForm") in root:
acroform = root[NameObject("/AcroForm")]
if NameObject("/Fields") in acroform:
# Replace with empty array to clear all fields
acroform[NameObject("/Fields")] = ArrayObject()
# Also clear widget annotations from each page
for i in range(len(self.writer.pages)):
page = self.writer.pages[i]
if NameObject("/Annots") in page:
page[NameObject("/Annots")] = ArrayObject()
def add_text_box(
self,
name: str,
page: int,
bounding_box: BoundingBox,
multiline: bool = False,
) -> None:
rect = rect_for(bounding_box, self.writer.pages[page])
textbox = Textbox(name=name, rect=rect, multiline=multiline)
self.writer.add_annotation(page_number=page, annotation=textbox)
def add_checkbox(self, name: str, page: int, bounding_box: BoundingBox) -> None:
rect = rect_for(bounding_box, self.writer.pages[page])
checkbox = Checkbox(name=name, rect=rect)
self.writer.add_annotation(page_number=page, annotation=checkbox)
def add_signature(self, name: str, page: int, bounding_box: BoundingBox) -> None:
rect = rect_for(bounding_box, self.writer.pages[page])
signature = Signature(name=name, rect=rect)
self.writer.add_annotation(page_number=page, annotation=signature)
def save(self, output_path: str) -> None:
self.writer.reattach_fields()
with open(output_path, "wb") as fp:
self.writer.write(fp)
def close(self) -> None:
self.writer.close()
self.reader.close()
```
## /commonforms/inference.py
```py path="/commonforms/inference.py"
from __future__ import annotations
from ultralytics import YOLO
from pathlib import Path
from huggingface_hub import hf_hub_download
from commonforms.utils import BoundingBox, Page, Widget
from commonforms.form_creator import PyPdfFormCreator
from commonforms.exceptions import EncryptedPdfError
import formalpdf
import pypdfium2
# our mapping from (model_name, fast) to (repo_id, filename) for the huggingface hub
models = {
("FFDNET-S", True): ("jbarrow/FFDNet-S-cpu", "FFDNet-S.onnx"),
("FFDNET-S", False): ("jbarrow/FFDNet-S", "FFDNet-S.pt"),
("FFDNET-L", True): ("jbarrow/FFDNet-L-cpu", "FFDNet-L.onnx"),
("FFDNET-L", False): ("jbarrow/FFDNet-L", "FFDNet-L.pt"),
}
class FFDNetDetector:
def __init__(
self, model_or_path: str, device: int | str = "cpu", fast: bool = False
) -> None:
self.device = device
self.fast = fast
model_path = self.get_model_path(model_or_path, device, fast)
self.model = YOLO(model_path, task="detect")
self.id_to_cls = {0: "TextBox", 1: "ChoiceButton", 2: "Signature"}
def get_model_path(
self, model_or_path: str, device: int | str = "cpu", fast: bool = False
) -> str:
"""
Construct the path to the model weights based on:
(a) the requested model (in the package or external path)
(b) --fast (if enabled, use ONNX, otherwise use pt)
"""
model_upper = model_or_path.upper()
if model_upper in ["FFDNET-S", "FFDNET-L"]:
# download the model, will just use the cached version if it already exists
repo_id, filename = models[(model_upper, fast)]
model_path = hf_hub_download(repo_id=repo_id, filename=filename)
else:
model_path = model_or_path
return model_path
def extract_widgets(
self, pages: list[Page], confidence: float = 0.3, image_size: int = 1600
) -> dict[int, list[Widget]]:
if self.fast:
# overrides the image size to 1216, since that's all ONNX supports
results = [
self.model.predict(
p.image, iou=1, conf=confidence, augment=False, imgsz=1216
)
for p in pages
]
else:
results = self.model.predict(
[p.image for p in pages],
iou=0.1,
conf=confidence,
augment=True,
imgsz=image_size,
device=self.device,
)
widgets = {}
for page_ix, result in enumerate(results):
if isinstance(result, list):
result = result[0]
# no predictions, skip page
if result is None or result.boxes is None:
continue
widgets[page_ix] = []
for box in result.boxes.cpu().numpy():
x, y, w, h = box.xywhn[0]
cls_id = int(box.cls.item())
widget_type = self.id_to_cls[cls_id]
widgets[page_ix].append(
Widget(
widget_type=widget_type,
bounding_box=BoundingBox.from_yolo(cx=x, cy=y, w=w, h=h),
page=page_ix,
)
)
# do our best to sort the widgets into something resembling reading
# order; this is important for being able to Tab/Shift-Tab back and
# forth to navigate the page.
widgets[page_ix] = sort_widgets(widgets[page_ix])
return widgets
def sort_widgets(widgets: list[Widget]) -> list[Widget]:
"""
Sort widgets in approximate reading order (left-to-right/top-to-bottom)
which makes the LLMs less likely to mess up.
"""
# Sort first by y coordinate, then x coordinate for reading order
sorted_widgets = sorted(
widgets,
key=lambda w: (
round(
w.bounding_box.y0, 3
), # Round to handle minor vertical alignment differences
w.bounding_box.x0,
),
)
# Find rows of widgets by grouping those with similar y coordinates
y_threshold = 0.01 # Threshold for considering widgets on same line
lines = []
current_line = []
for widget in sorted_widgets:
if (
not current_line
or abs(widget.bounding_box.y0 - current_line[0].bounding_box.y0)
< y_threshold
):
current_line.append(widget)
else:
# Sort widgets in line by x coordinate
current_line.sort(key=lambda w: w.bounding_box.x0)
lines.append(current_line)
current_line = [widget]
if current_line:
current_line.sort(key=lambda w: w.bounding_box.x0)
lines.append(current_line)
# Flatten the lines back into single list
return [widget for line in lines for widget in line]
def render_pdf(pdf_path: str) -> list[Page]:
pages = []
doc = formalpdf.open(pdf_path)
try:
for page in doc:
image = page.render()
pages.append(Page(image=image, width=image.width, height=image.height))
return pages
finally:
doc.document.close()
def prepare_form(
input_path: str | Path,
output_path: str | Path,
*,
model_or_path: str = "FFDNet-L",
keep_existing_fields: bool = False,
use_signature_fields: bool = False,
device: int | str = "cpu",
image_size: int = 1600,
confidence: float = 0.3,
fast: bool = False,
multiline: bool = False,
):
detector = FFDNetDetector(model_or_path, device=device, fast=fast)
try:
pages = render_pdf(input_path)
except pypdfium2._helpers.misc.PdfiumError:
raise EncryptedPdfError
results = detector.extract_widgets(
pages, confidence=confidence, image_size=image_size
)
writer = PyPdfFormCreator(input_path)
if not keep_existing_fields:
writer.clear_existing_fields()
for page_ix, widgets in results.items():
for i, widget in enumerate(widgets):
name = f"{widget.widget_type.lower()}_{widget.page}_{i}"
if widget.widget_type == "TextBox":
writer.add_text_box(name, page_ix, widget.bounding_box, multiline=multiline)
elif widget.widget_type == "ChoiceButton":
writer.add_checkbox(name, page_ix, widget.bounding_box)
elif widget.widget_type == "Signature":
if use_signature_fields:
writer.add_signature(name, page_ix, widget.bounding_box)
else:
writer.add_text_box(name, page_ix, widget.bounding_box)
writer.save(output_path)
writer.close()
```
## /commonforms/utils.py
```py path="/commonforms/utils.py"
from __future__ import annotations
from typing import Literal
from pydantic import BaseModel
from dataclasses import dataclass
from PIL import Image
class BoundingBox(BaseModel):
x0: float
y0: float
x1: float
y1: float
@classmethod
def from_yolo(cls, cx: float, cy: float, w: float, h: float) -> BoundingBox:
return cls(x0=cx - w / 2, y0=cy - h / 2, x1=cx + w / 2, y1=cy + h / 2)
class Widget(BaseModel):
widget_type: Literal[
"TextBox",
"ChoiceButton",
"Signature",
]
bounding_box: BoundingBox
page: int
@dataclass
class Page:
image: Image.Image
width: float
height: float
```
## /dataset/generate_coco.py
```py path="/dataset/generate_coco.py"
import json
import sys
from pathlib import Path
from concurrent.futures import ProcessPoolExecutor, as_completed
import formalpdf
import logging
import pypdfium2.raw as pdfium_c
import ctypes
logging.getLogger("pypdfium2").setLevel(logging.ERROR)
def process_pdf(pdf_path, output_dir):
"""Process all pages of a PDF and generate JSON annotation files"""
json_dir = output_dir / "json"
images_dir = output_dir / "images"
pdf_name = pdf_path.stem
# Check if first page JSON exists - if so, skip entire PDF
first_page_json = json_dir / f"{pdf_name}-0.json"
if first_page_json.exists():
return f"Skipped {pdf_name} (already processed)"
try:
document = formalpdf.open(str(pdf_path))
num_pages = len(document)
total_widgets = 0
for page_idx in range(num_pages):
page = document[page_idx]
pdfium_page = document.document[page_idx]
width_pt, height_pt = pdfium_page.get_size()
target_px = 1680
# Scale based on the smaller dimension
scale = target_px / min(width_pt, height_pt)
image = pdfium_page.render(scale=scale, may_draw_forms=False).to_pil()
widgets = page.widgets()
image_filename = f"{pdf_name}-{page_idx}.jpg"
# Create image info
image_info = {
"file_name": image_filename,
"width": image.width,
"height": image.height,
}
# Save image
image.save(images_dir / image_filename, format="JPEG")
# Process annotations
annotations = []
for widget in widgets:
# Use pypdfium2's page-to-device coordinate transformation
# to properly convert PDF coordinates to image pixel coordinates
page_x1, page_y1 = widget.rect.left, widget.rect.bottom
page_x2, page_y2 = widget.rect.right, widget.rect.top
# Convert page coordinates to device coordinates
# using pypdfium2's FPDF_PageToDevice function
dev_x1 = ctypes.c_int()
dev_y1 = ctypes.c_int()
dev_x2 = ctypes.c_int()
dev_y2 = ctypes.c_int()
# FPDF_PageToDevice(page, start_x, start_y, size_x, size_y, rotate, page_x, page_y, device_x, device_y)
pdfium_c.FPDF_PageToDevice(
pdfium_page.raw, 0, 0, image.width, image.height, 0,
page_x1, page_y1, ctypes.byref(dev_x1), ctypes.byref(dev_y1)
)
pdfium_c.FPDF_PageToDevice(
pdfium_page.raw, 0, 0, image.width, image.height, 0,
page_x2, page_y2, ctypes.byref(dev_x2), ctypes.byref(dev_y2)
)
# Convert to Python ints
left = float(dev_x1.value)
bottom = float(dev_y1.value)
right = float(dev_x2.value)
top = float(dev_y2.value)
# Device coordinates have top-left origin, so y values are already correct
y0 = min(top, bottom)
y1 = max(top, bottom)
# try for the category, otherwise "Text"
categories = { "Text": 0,
"ComboBox": 0,
"CheckBox": 1,
"RadioButton": 1,
"Signature": 2,
"PushButton": 3,
"ListBox": 3,
"Unknown": 3 }
category_id = categories.get(widget.field_type_string, 3)
if category_id > 2:
continue
bbox_width = right - left
bbox_height = y1 - y0
annotations.append({
"category_id": category_id,
"bbox": [left, y0, bbox_width, bbox_height],
"area": bbox_width * bbox_height,
"iscrowd": 0,
"segmentation": [],
})
# Create per-page JSON
page_data = {
"image": image_info,
"annotations": annotations,
}
# Save JSON
json_path = json_dir / f"{pdf_name}-{page_idx}.json"
with json_path.open("w") as fp:
json.dump(page_data, fp, indent=2)
total_widgets += len(widgets)
return f"Processed {pdf_name}: {num_pages} pages, {total_widgets} widgets"
except Exception as e:
return f"Error processing {pdf_name}: {str(e)}"
finally:
document.document.close()
def main():
pdfs_dir = Path(sys.argv[1]) if len(sys.argv) > 1 else Path("pdfs")
output_dir = Path(sys.argv[2]) if len(sys.argv) > 2 else Path("coco")
json_dir = output_dir / "json"
images_dir = output_dir / "images"
# Create directories
output_dir.mkdir(exist_ok=True)
json_dir.mkdir(exist_ok=True)
images_dir.mkdir(exist_ok=True)
# Find all PDF files
pdf_files = list(pdfs_dir.rglob("*.pdf"))
total_pdfs = len(pdf_files)
print(f"Found {total_pdfs} PDF files")
# Check which PDFs are already processed
skipped_count = 0
tasks = []
for pdf_path in pdf_files:
pdf_name = pdf_path.stem
first_page_json = json_dir / f"{pdf_name}-0.json"
if first_page_json.exists():
skipped_count += 1
else:
tasks.append(pdf_path)
print(f"Already processed (skipped): {skipped_count} PDFs")
print(f"New PDFs to process: {len(tasks)}")
if tasks:
# Process PDFs in parallel
with ProcessPoolExecutor() as executor:
futures = {executor.submit(process_pdf, pdf_path, output_dir): pdf_path for pdf_path in tasks}
completed = 0
for future in as_completed(futures):
completed += 1
result = future.result()
print(f"[{completed}/{len(tasks)}] {result}")
if __name__ == "__main__":
main()
```
## /dataset/merge_coco.py
```py path="/dataset/merge_coco.py"
import json
import os
import sys
from pathlib import Path
def merge_coco_annotations():
"""Merge individual JSON files into a single COCO format annotations file"""
coco_dir = Path(sys.argv[1]) if len(sys.argv) > 1 else Path("coco")
json_dir = coco_dir / "json"
output_file = coco_dir / "annotations.json"
# COCO format structure
coco_data = {
"info": {
"year": 2025,
"version": "1.0",
"description": "Form field detection dataset",
"contributor": "",
"url": "",
"date_created": "2025-10-16"
},
"licenses": [
{
"id": 1,
"name": "Unknown",
"url": ""
}
],
"images": [],
"annotations": [],
"categories": [
{"id": 0, "name": "Text", "supercategory": "none"},
{"id": 1, "name": "CheckBox", "supercategory": "none"}
]
}
# Get all JSON files sorted by name
json_files = sorted(json_dir.glob("*.json"))
image_id = 0
annotation_id = 0
for json_file in json_files:
with json_file.open("r") as fp:
page_data = json.load(fp)
# Add image with sequential ID
image_info = page_data["image"].copy()
image_info["id"] = image_id
coco_data["images"].append(image_info)
# Track seen bounding boxes for this page to skip duplicates
seen_bboxes = set()
# Add annotations with sequential IDs and image_id reference
for annotation in page_data["annotations"]:
if json_file.name.startswith("2908641"):
continue
# Round bounding box to integers
bbox = annotation["bbox"]
bbox_int = [round(bbox[0]), round(bbox[1]), round(bbox[2]), round(bbox[3])]
# Skip if any x or y coordinate is negative
if bbox_int[0] < 0 or bbox_int[1] < 0:
continue
# Skip if bbox extends beyond image boundaries
if (bbox_int[0] + bbox_int[2] > image_info["width"] or
bbox_int[1] + bbox_int[3] > image_info["height"]):
continue
# Calculate area from rounded bounding box
area_int = bbox_int[2] * bbox_int[3]
bbox_tuple = tuple(bbox_int)
# Skip if this bounding box was already added for this page
if bbox_tuple in seen_bboxes:
continue
seen_bboxes.add(bbox_tuple)
annotation_copy = annotation.copy()
annotation_copy["id"] = annotation_id
annotation_copy["image_id"] = image_id
annotation_copy["bbox"] = bbox_int
annotation_copy["area"] = area_int
coco_data["annotations"].append(annotation_copy)
annotation_id += 1
image_id += 1
# Save merged COCO format file
with output_file.open("w") as fp:
json.dump(coco_data, fp, indent=2)
print(f"Merged {len(coco_data['images'])} images with {len(coco_data['annotations'])} annotations")
print(f"Saved to {output_file}")
# Create symlink in images folder
images_dir = coco_dir / "images"
symlink_path = images_dir / "_annotations.coco.json"
# Remove existing symlink if it exists
if symlink_path.exists() or symlink_path.is_symlink():
symlink_path.unlink()
# Create relative symlink
os.symlink(os.path.relpath(output_file, images_dir), symlink_path)
print(f"Created symlink at {symlink_path}")
if __name__ == "__main__":
merge_coco_annotations()
```
## /dataset/split_dataset.py
```py path="/dataset/split_dataset.py"
import csv
import shutil
import sys
from pathlib import Path
def read_csv_ids(csv_path):
ids = []
with open(csv_path, 'r') as f:
reader = csv.reader(f)
for row in reader:
if row:
id_value = row[0].strip()
ids.append(id_value)
return ids
def read_all_filenames(coco_dir):
"""Read all filenames once into arrays"""
images_dir = coco_dir / 'images'
json_dir = coco_dir / 'json'
# Get all filenames as lists
image_files = [f for f in images_dir.iterdir() if f.is_file()]
json_files = [f for f in json_dir.iterdir() if f.is_file()]
return image_files, json_files
def build_file_lookup(image_files, json_files):
from collections import defaultdict
image_lookup = defaultdict(list)
json_lookup = defaultdict(list)
for img_file in image_files:
# Extract ID from filename (ID-pagenumber.ext pattern)
id_value = img_file.name.split('-')[0]
image_lookup[id_value].append(img_file)
for json_file in json_files:
# Extract ID from filename (ID-pagenumber.ext pattern)
id_value = json_file.name.split('-')[0]
json_lookup[id_value].append(json_file)
return image_lookup, json_lookup
def move_files(source_files, dest_dir):
dest_dir.mkdir(parents=True, exist_ok=True)
moved_count = 0
for file_path in source_files:
dest_path = dest_dir / file_path.name
try:
shutil.move(str(file_path), str(dest_path))
moved_count += 1
print(f"Moved: {file_path.name}")
except Exception as e:
print(f"Error moving {file_path}: {e}")
return moved_count
def main():
coco_dir = Path(sys.argv[1])
# Validate inputs
if not coco_dir.exists():
print(f"Error: Directory '{coco_dir}' does not exist")
return 1
images_dir = coco_dir / 'images'
json_dir = coco_dir / 'json'
if not images_dir.exists() or not json_dir.exists():
print(f"Error: Directory must contain 'images' and 'json' subdirectories")
return 1
# Read CSV files
test_csv = Path('test.csv')
val_csv = Path('val.csv')
if not test_csv.exists() or not val_csv.exists():
print("Error: test.csv and val.csv must exist in current directory")
return 1
print(f"Reading test IDs from {test_csv}...")
test_ids = read_csv_ids(test_csv)
print(f"Found {len(test_ids)} test IDs")
print(f"\nReading validation IDs from {val_csv}...")
val_ids = read_csv_ids(val_csv)
print(f"Found {len(val_ids)} validation IDs")
# Create test and val directories as siblings to coco_dir
test_dir = coco_dir.parent / 'test'
val_dir = coco_dir.parent / 'val'
test_images_dir = test_dir / 'images'
test_json_dir = test_dir / 'json'
val_images_dir = val_dir / 'images'
val_json_dir = val_dir / 'json'
# Read all filenames once into memory and build lookup
print("\nReading all filenames from coco directory...")
image_files, json_files = read_all_filenames(coco_dir)
print(f"Found {len(image_files)} images and {len(json_files)} JSON files")
print("Building file lookup index...")
image_lookup, json_lookup = build_file_lookup(image_files, json_files)
print(f"Indexed {len(image_lookup)} unique image IDs and {len(json_lookup)} unique JSON IDs")
# Process test files
print("\n" + "="*60)
print("Processing TEST files...")
print("="*60)
test_image_count = 0
test_json_count = 0
for id_value in test_ids:
matching_images = image_lookup.get(id_value, [])
matching_json = json_lookup.get(id_value, [])
if matching_images or matching_json:
test_image_count += move_files(matching_images, test_images_dir)
test_json_count += move_files(matching_json, test_json_dir)
# Process validation files
print("\n" + "="*60)
print("Processing VALIDATION files...")
print("="*60)
val_image_count = 0
val_json_count = 0
for id_value in val_ids:
matching_images = image_lookup.get(id_value, [])
matching_json = json_lookup.get(id_value, [])
if matching_images or matching_json:
val_image_count += move_files(matching_images, val_images_dir)
val_json_count += move_files(matching_json, val_json_dir)
# Summary
print("\n" + "="*60)
print("SUMMARY")
print("="*60)
print(f"Test images moved: {test_image_count}")
print(f"Test JSON files moved: {test_json_count}")
print(f"Validation images moved: {val_image_count}")
print(f"Validation JSON files moved: {val_json_count}")
return 0
if __name__ == '__main__':
main()
```
## /dataset/test.csv
```csv path="/dataset/test.csv"
3080814
4066548
6163967
6186894
2632049
7182331
3356313
5061170
3038685
6679579
6037783
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5182793
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3766312
6187813
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3007193
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2804127
7883315
3357457
7545260
0309383
6056985
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4083411
3356545
3742941
7516639
4518559
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4880021
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5259150
5484837
3157025
3808204
5123750
5758693
3243052
0759460
0277709
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6087617
7854208
7392292
4626849
6843297
0957126
7888595
1836022
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1680293
3254294
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3712343
3449104
4197681
2153869
5436488
5379307
1273835
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3017241
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4501339
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0314011
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0541705
4039254
2285521
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3352548
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7329018
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7181180
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4654023
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2173792
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2562603
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5104620
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3393935
7320828
6448912
1830500
0581402
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0575701
0442193
5146404
1298540
3874134
2444665
2456634
1417798
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6774291
2882040
1693380
2158811
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0257464
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2951409
3168552
2582651
4652080
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4219736
2217249
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6463421
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2687213
5109631
0526076
3876911
5514692
6525170
0223626
6379660
2534198
3809424
1172414
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5189598
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4027913
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1746223
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5491163
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4408599
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6611347
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3810343
7215076
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0950251
2960804
0190486
6563315
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4002487
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2305551
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0772280
1758646
7504734
1049293
0161158
2369661
1802179
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1514898
3373274
1652878
6451193
6455034
4733315
0865436
3920879
3045532
4873987
0487885
2742776
0056586
2104568
5476714
5608551
5782496
3898186
0813998
1190137
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2319575
2656019
1023768
7314519
7743419
3058915
7582785
7328532
4801129
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0913553
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4492855
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1233621
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5044830
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0322490
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6581672
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4525414
1893177
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2882291
2100826
7281489
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1218764
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3594736
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0141545
4171334
0228426
3801290
4333769
4371186
2948246
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1180466
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0091418
3712708
3749467
6370403
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2796246
2499863
4040437
6048634
1758383
4003467
2730170
0087271
5170685
4466279
2673809
7639191
1520938
6841428
1222744
3455789
1079722
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2128618
5777892
6228631
5543749
2276376
2719075
2519793
3453138
2000176
2894342
5557089
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6256232
2156509
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0742146
2780556
7521790
3043438
0335430
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3700870
3663249
6478871
0880449
2667376
1474296
3702469
4848673
1825584
6898924
7309777
3353045
3661806
1158191
2443506
7718522
2765346
6862645
5045142
4456132
2513973
7361105
4600616
1044906
0539169
2635450
5450950
0672567
1482300
4533025
1230598
6895205
6342437
4558397
7862317
3554102
5984615
2910756
0248826
6175064
1841592
2637605
5507946
1000873
1371487
1603247
4403935
7016988
3143454
6710565
1718852
4314509
0196565
7416729
4554710
5972885
6010863
3882311
3756407
5767969
4969089
3650778
1479147
7721741
4176864
4994914
2579400
6893239
0794707
7527496
6886262
6197376
3543780
5097713
0305651
0418999
7816618
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0261129
```
## /dataset/val.csv
```csv path="/dataset/val.csv"
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4845849
3501175
3603621
7872525
0168404
1731311
5711805
3654293
2581124
3089997
3399746
6088399
7392610
7496330
1953009
1468419
5996822
2399568
0212670
6375588
7122125
7418456
2806815
5679693
0813709
2332953
6078218
1099287
1646392
5779162
7074416
7227495
3452530
5511311
1678099
5441606
2137785
5862817
7639044
4243504
6957996
7065210
5403618
3233027
6734009
2933672
4056067
0902005
2013190
4806035
2352853
5339200
7407021
5863791
4534575
3526072
5037695
5796531
2458622
4931180
0383986
5303974
5476597
4830221
6111532
2592424
1397629
6896375
2888834
1819828
6136735
2506231
3264786
3896312
6733549
4731109
5660466
1324153
3444097
0870363
7023140
1011988
1854085
2882336
2142470
1634751
3159193
3035608
6941672
2787135
1860199
2633704
0350874
2302254
6900894
6396842
6980680
4897135
4442786
2613357
4225298
4661502
0586230
3562214
1761176
2152888
4537763
2296003
5978342
6153821
4681666
5107483
6975581
2120309
3734419
4289735
6874458
1154075
2483824
2071993
3008898
3528262
7050739
6076269
2544262
5304365
6996039
1222377
6598303
3242157
7508405
5803199
2460144
5519246
2502100
4546275
1205463
0645917
7457301
3392688
1086721
2382440
0576332
0019944
6003168
7424667
4331240
0991115
6387781
4697231
6624749
7398902
6837870
1596058
0172096
7420016
1998476
1505013
0352578
1986456
0292807
5037341
2026787
0349397
4250143
4169735
2045966
2219053
3520725
5485321
0732404
7097593
4260515
1690043
5426135
4918493
1456498
1021628
4588859
7490237
0620411
7369688
2559588
3624944
0478079
5711634
7239497
2778842
4210558
4244044
0747333
7741813
4646507
7592933
5107870
7512174
6735487
4132963
6166833
7862216
5079571
4266764
2624332
7655311
0361504
5019620
7359174
2760837
6367868
0733392
5148403
5800732
6009115
6202419
6554363
5243653
3572218
5251988
4531836
3177598
2095449
5974164
4392082
6413052
3021170
3326440
1303807
3253109
5973133
5829617
3261396
6174761
1956959
1634402
1666950
2713566
3098934
3994343
5277444
4930597
7586961
4776555
0524076
5659003
5759465
4840327
5012263
6584366
5042585
3133048
6046128
0454548
3053750
2764623
4786217
5274732
7918207
3009860
3595830
2450020
5116481
7170594
1716669
0795432
4007415
3762457
0899437
6493678
6682829
3309819
7709051
6340621
7564013
5824616
0724401
0912004
6041405
1857895
0955479
0090509
5393495
6492838
2580439
1077993
3110536
3922250
2686550
6550518
1052089
6212422
4515541
1111382
0517530
4104588
4072588
1438988
5073070
3166354
5354628
6762783
0636657
6222655
1407942
3053004
4090391
4062570
3819214
1002781
7455027
1793753
3891209
3863924
5379957
1257381
4257654
5298655
3167522
1658509
5022840
0429514
2435368
3479433
5265932
4759330
6263734
4715354
0977749
7680395
6895226
3664002
5770510
2114178
2625928
6171932
3361297
0013995
3341268
2495387
2215160
0116177
2252714
2828179
5538659
4127866
0312433
7342021
4848897
3087474
7441255
3503486
7876898
5868676
1074298
0504691
5251700
0537821
0685174
5076819
2907364
6856884
2959786
1617775
2233432
1858650
6564773
3443855
2632218
6848626
6919846
7230439
4763962
0756688
5663849
1447586
3674433
6723417
4866262
6948660
4845995
7299906
1957278
7009281
6850762
7281279
5226386
6490316
6613847
7015098
2661365
4523472
6221990
6359708
6017516
1867833
7847539
6199727
0326525
6033393
3117631
1302691
5983614
3328419
1070889
0461480
0916333
0259855
4734778
2618434
5801384
7267832
3385735
0429913
2835478
4993177
0243193
3531013
5609356
2896038
3468668
3748551
7673307
7727510
```
## /pyproject.toml
```toml path="/pyproject.toml"
[build-system]
requires = ["setuptools>=61.0"]
build-backend = "setuptools.build_meta"
[project]
name = "commonforms"
version = "0.1.6"
description = "Automatically convert a PDF into a fillable form"
readme = "README.md"
requires-python = ">=3.10"
urls = { Homepage = "https://github.com/jbarrow/commonforms" }
dependencies = [
"cryptography>=3.1",
"formalpdf==0.1.6",
"huggingface-hub>=0.35.3",
"onnx>=1.19.1",
"onnxruntime>=1.23.1",
"onnxslim>=0.1.71",
"pillow>=11.3.0",
"pydantic>=2.11.9",
"pypdf>=6.1.1",
"ultralytics>=8.3.204",
]
[project.scripts]
commonforms = "commonforms:main"
[tool.setuptools]
packages = ["commonforms"]
[dependency-groups]
dev = [
"pytest>=8.4.2",
"ruff>=0.14.0",
]
```
## /tests/inference_test.py
```py path="/tests/inference_test.py"
import commonforms
import commonforms.exceptions
import formalpdf
import pytest
def test_inference(tmp_path):
# tmp_path is a built-in pythest fixture where we'll write the outputs
output_path = tmp_path / "output.pdf"
commonforms.prepare_form("./tests/resources/input.pdf", output_path)
assert output_path.exists()
doc = formalpdf.open(output_path)
assert len(doc[0].widgets()) > 0
doc.document.close()
def test_inference_fast(tmp_path):
output_path = tmp_path / "output.pdf"
commonforms.prepare_form("./tests/resources/input.pdf", output_path, fast=True)
assert output_path.exists()
doc = formalpdf.open(output_path)
assert len(doc[0].widgets()) > 0
doc.document.close()
def test_mutlinline(tmp_path):
output_path = tmp_path / "output.pdf"
commonforms.prepare_form("./tests/resources/input.pdf", output_path, fast=True, multiline=True)
assert output_path.exists()
doc = formalpdf.open(output_path)
assert len(doc[0].widgets()) > 0
doc.document.close()
def test_encrypted_failure(tmp_path):
# Reminder to future Joe: password for encrypted PDF is "kanbanery"
output_path = tmp_path / "output.pdf"
with pytest.raises(commonforms.exceptions.EncryptedPdfError):
commonforms.prepare_form("./tests/resources/encrypted.pdf", output_path)
# TODO(joe): future tests around handling encrypted PDFs
# 1. add a --password flag and test that inference doesn't fail
# 2. if a password is provided, ensure that the _output_ PDF remains encrpyted
# with the same password
```
## /tests/resources/encrypted.pdf
Binary file available at https://raw.githubusercontent.com/jbarrow/commonforms/refs/heads/main/tests/resources/encrypted.pdf
## /tests/resources/input.pdf
Binary file available at https://raw.githubusercontent.com/jbarrow/commonforms/refs/heads/main/tests/resources/input.pdf
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