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ebinan92
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Model Details
Model Provider
ebinan92
Model Tree
Qwen/Qwen3.5-4B
Input Modalities
Output Modalities
Supported Functionality
json
[ { "bbox": [84, 107, 912, 168], "type": "handwritten", "text": "Приклад рукописного тексту" }]bbox is [x1, y1, x2, y2] in normalized 0..1000 coordinates. Valid types
are handwritten, printed, formula, table, annotation, image, and
graph. Formula text uses LaTeX; table text is pipe-separated; image and
graph use empty text.
Use Transformers 5.8.1 or newer. The exact prompt used for training is included below and should be kept unchanged.
python
from PIL import Imageimport torchfrom transformers import AutoModelForMultimodalLM, AutoProcessor MODEL_ID = "ebinan92/Rukopys-OCR-4B"PROMPT = ( "Detect every text region in this Ukrainian handwritten document and " "return a JSON array of regions. Each region has bbox (x1 y1 x2 y2 in " "0..1000 normalized image coordinates), type (handwritten | printed | " "formula | table | annotation | image | graph), and text (transcription; " "empty for image/graph; LaTeX for formula; pipe-separated for table).") processor = AutoProcessor.from_pretrained(MODEL_ID)model = AutoModelForMultimodalLM.from_pretrained( MODEL_ID, dtype=torch.bfloat16, device_map="auto")image = Image.open("document.jpg").convert("RGB")messages = [{ "role": "user", "content": [{"type": "image"}, {"type": "text", "text": PROMPT}],}]text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device) with torch.inference_mode(): output_ids = model.generate(**inputs, max_new_tokens=8192, do_sample=False) new_tokens = output_ids[:, inputs["input_ids"].shape[1]:]print(processor.batch_decode(new_tokens, skip_special_tokens=True)[0])python
from PIL import Imagefrom transformers import AutoProcessorfrom vllm import LLM, SamplingParams MODEL_ID = "ebinan92/Rukopys-OCR-4B"PROMPT = ( "Detect every text region in this Ukrainian handwritten document and " "return a JSON array of regions. Each region has bbox (x1 y1 x2 y2 in " "0..1000 normalized image coordinates), type (handwritten | printed | " "formula | table | annotation | image | graph), and text (transcription; " "empty for image/graph; LaTeX for formula; pipe-separated for table).") processor = AutoProcessor.from_pretrained(MODEL_ID)image = Image.open("document.jpg").convert("RGB")messages = [{ "role": "user", "content": [ {"type": "image", "image": image}, {"type": "text", "text": PROMPT}, ],}]prompt = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False) factor = processor.image_processor.patch_size * processor.image_processor.merge_sizellm = LLM( model=MODEL_ID, dtype="bfloat16", max_model_len=16384, limit_mm_per_prompt={"image": 1}, mm_processor_kwargs={ "min_pixels": 256 * factor * factor, "max_pixels": 4096 * factor * factor, },)params = SamplingParams(max_tokens=8192, temperature=0.0)outputs = llm.generate( [{"prompt": prompt, "multi_modal_data": {"image": image}}], sampling_params=params,)print(outputs[0].outputs[0].text)Training used RUKOPYS gold/silver data, external Cyrillic handwriting data,
and pseudo-labels, some of which were generated with Gemini
(gemini-3-flash-preview).
| Dataset | License |
|---|---|
| RUKOPYS | CC BY 4.0 |
| Ukrainian Handwritten Text | CC BY-SA 4.0 |
| school_notebooks_RU | MIT |
| HWR200 | Apache-2.0 |
The model weights are released under the Apache License 2.0. Training datasets are not redistributed here and remain subject to their own licenses.
bibtex
@misc{ebinan2026rukopysocr4b, title = {Rukopys-OCR-4B: Ukrainian Handwritten Document OCR}, author = {ebinan92}, year = {2026}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/ebinan92/Rukopys-OCR-4B}}}[ { "bbox": [84, 107, 912, 168], "type": "handwritten", "text": "Приклад рукописного тексту" }]from PIL import Imageimport torchfrom transformers import AutoModelForMultimodalLM, AutoProcessor MODEL_ID = "ebinan92/Rukopys-OCR-4B"PROMPT = ( "Detect every text region in this Ukrainian handwritten document and " "return a JSON array of regions. Each region has bbox (x1 y1 x2 y2 in " "0..1000 normalized image coordinates), type (handwritten | printed | " "formula | table | annotation | image | graph), and text (transcription; " "empty for image/graph; LaTeX for formula; pipe-separated for table).") processor = AutoProcessor.from_pretrained(MODEL_ID)model = AutoModelForMultimodalLM.from_pretrained( MODEL_ID, dtype=torch.bfloat16, device_map="auto")image = Image.open("document.jpg").convert("RGB")messages = [{ "role": "user", "content": [{"type": "image"}, {"type": "text", "text": PROMPT}],}]text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device) with torch.inference_mode(): output_ids = model.generate(**inputs, max_new_tokens=8192, do_sample=False) new_tokens = output_ids[:, inputs["input_ids"].shape[1]:]print(processor.batch_decode(new_tokens, skip_special_tokens=True)[0])from PIL import Imagefrom transformers import AutoProcessorfrom vllm import LLM, SamplingParams MODEL_ID = "ebinan92/Rukopys-OCR-4B"PROMPT = ( "Detect every text region in this Ukrainian handwritten document and " "return a JSON array of regions. Each region has bbox (x1 y1 x2 y2 in " "0..1000 normalized image coordinates), type (handwritten | printed | " "formula | table | annotation | image | graph), and text (transcription; " "empty for image/graph; LaTeX for formula; pipe-separated for table).") processor = AutoProcessor.from_pretrained(MODEL_ID)image = Image.open("document.jpg").convert("RGB")messages = [{ "role": "user", "content": [ {"type": "image", "image": image}, {"type": "text", "text": PROMPT}, ],}]prompt = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False) factor = processor.image_processor.patch_size * processor.image_processor.merge_sizellm = LLM( model=MODEL_ID, dtype="bfloat16", max_model_len=16384, limit_mm_per_prompt={"image": 1}, mm_processor_kwargs={ "min_pixels": 256 * factor * factor, "max_pixels": 4096 * factor * factor, },)params = SamplingParams(max_tokens=8192, temperature=0.0)outputs = llm.generate( [{"prompt": prompt, "multi_modal_data": {"image": image}}], sampling_params=params,)print(outputs[0].outputs[0].text)@misc{ebinan2026rukopysocr4b, title = {Rukopys-OCR-4B: Ukrainian Handwritten Document OCR}, author = {ebinan92}, year = {2026}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/ebinan92/Rukopys-OCR-4B}}}