AlexandreSheva/rukopys-yolo11m-detector contains an Ultralytics YOLO 11M detector trained to localize handwritten regions in RUKOPYS manuscript page images. It is the layout-detection component of the RUKOPYS HTR pipeline and is intended to produce bounding boxes that can be passed to a recognizer or combined with page-level vision-language predictions.

What It Does

Detects handwritten text regions on scanned Ukrainian manuscript pages.
Outputs YOLO object-detection boxes for one class: handwritten.
Fits the RUKOPYS pipeline as the detector used before crop-level or page-level transcription.
Supports reproducible experiments with the curated RUKOPYS MVP YOLO dataset.

Quick Use

python

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

model_path = hf_hub_download(
    repo_id="AlexandreSheva/rukopys-yolo11m-detector",
    filename="weights/best.pt",
)
model = YOLO(model_path)
results = model.predict("page.jpg", imgsz=1536)

Limitations

This model detects regions only; it does not transcribe text. It was trained for RUKOPYS-style Ukrainian manuscript pages, so validate it on other archives, scan qualities, and layouts before reuse. The detector is based on Ultralytics YOLO11 under AGPL-3.0, and the training data inherits CC BY-NC-SA 4.0 terms from the source dataset.

Project Context

This model is part of a practical HTR system: raw RUKOPYS data curation, dataset packaging, LoRA fine-tuning, inference, evaluation, and Kaggle-ready submission export. The goal is not only a checkpoint, but a reproducible document-AI workflow for Ukrainian handwritten archives.