YOLO11m Widget Detector

YOLO11m Widget Detector is a lightweight, high-performance document widget detector designed for scanned forms and PDFs.

The model detects three common form widget types:

text_input
choice_button
signature

It is optimized for:

scanned forms
enterprise PDFs
OCR pipelines
intelligent document processing (IDP)
form digitization workflows

The detector supports both CPU and GPU inference and can process PDFs or images directly.

Features

Detects form fields from scanned PDFs and images
Supports text boxes, checkboxes/radio buttons, and signatures
Works directly on PDFs
Optimized for document layouts
JSON export support
CPU and GPU compatible
Hugging Face auto-download support

Results

Model	Text	Choice	Signature	mAP@50 (↑)
YOLO11m v3 (1024px)	81.4	70.9	83.8	78.7
YOLO11m v4 (1024px)	83.9	72.1	86.6	80.9

Installation

The psynx-widget-detector package can be installed with either uv or pip, feel free to choose your package manager flavor. The uv command:

uv pip install psynx-widget-detector

The pip command:

pip install psynx-widget-detector

Once it's installed, you should be able to run inference on ~any PDF.

Python API

The simplest usage will run inference using the default suggested settings. The model weights will automatically download from Hugging Face on your first run.

from widget_detector import WidgetDetector

# Initialize the detector
# (Downloads PSynx/widget-detector-yolo automatically)
detector = WidgetDetector(
    conf=0.25,        # Confidence threshold
    iou=0.45,         # NMS IoU threshold
    imgsz=1024,       # Inference resolution
    device="cpu"      # "cuda" for GPU, "cpu" for CPU
)

# Process a PDF or Image
result = detector.detect_path("input.pdf")

# Print results
for page in result.pages:
    print(f"Page {page.page}: Found {len(page.widgets)} widgets")
    for w in page.widgets:
        print(f" - {w.class_name} ({w.confidence:.2f})")

# Save output to JSON
result.save("output.json")

Example Input and Output

Here is an example of a document before and after widget detection:

Input Document:

Detection Output:

References

CommonForms: A Large, Diverse Dataset for Form Field Detection

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