VietAlphaLabs/SenOCR-Vi

Research page · VietAlpha Lab

SenOCR-Vi is a Vietnamese-specialized document OCR model built on PaddleOCR-VL-1.6.

The model retains the approximately 0.96B-parameter PaddleOCR-VL-1.6 architecture while specializing recognition for Vietnamese documents. On the corrected 160-page Vietnamese evaluation population, SenOCR-Vi reaches 82.83 Vietnamese document composite and 86.7% Vietnamese text recognition under 1 - Edit_dist.

Fine-tuning updated 12.09M parameters, 1.26% of the model, and completed on a single NVIDIA A10G in about two hours.

SenOCR-Vi is intended primarily for Vietnamese printed documents, photographed pages, archival material, textbooks, and document-ingestion workflows.

Highlights

• Vietnamese-first accuracy: 82.83 Vietnamese document composite and 86.7% Vietnamese text recognition under 1 - Edit_dist, 1.93 points ahead of the PaddleOCR-VL-1.6 base on Vietnamese.
• Small and self-contained: 0.959B total parameters in a single merged FP32 checkpoint — no separate adapter branch is needed at inference.
• Cheap to reproduce: decoder-only LoRA rank 32 / alpha 64 over 12.09M trainable parameters (1.2614% of the model), trained on 1 x NVIDIA A10G in about 2 h 01 m.
• Verified merge: 128/128 exact decoded matches between base-plus-adapter inference and the merged release in FP32 merge-equivalence validation.
• Drop-in interface: identical to PaddleOCR-VL-1.6 — usable through transformers for element-level recognition, or as the VLM recognition model inside the PaddleOCR-VL 1.6 page-parsing pipeline.
• Permissive Apache 2.0 license: commercial use, customization and redistribution without copyleft restrictions.

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Inference examples

SenOCR-Vi follows the PaddleOCR-VL-1.6 model interface.

Transformers

For direct OCR recognition:

pip install "transformers>=5.0.0" torch pillow

from PIL import Image
import torch
from transformers import AutoProcessor, AutoModelForImageTextToText

model_id = "VietAlphaLabs/SenOCR-Vi"
image_path = "document.png"

device = "cuda" if torch.cuda.is_available() else "cpu"

model = (
    AutoModelForImageTextToText
    .from_pretrained(model_id, torch_dtype=torch.float32)
    .to(device)
    .eval()
)
processor = AutoProcessor.from_pretrained(model_id)

image = Image.open(image_path).convert("RGB")

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {"type": "text", "text": "OCR:"},
        ],
    }
]

inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(device)

outputs = model.generate(**inputs, max_new_tokens=512)
text = processor.decode(
    outputs[0][inputs["input_ids"].shape[-1]:-1]
)

print(text)

The qualified benchmark artifact was served in FP32. Lower-precision deployment should be validated separately for the target environment.

PaddleOCR

For page-level document parsing, use SenOCR-Vi as the VLM recognition model inside the PaddleOCR-VL 1.6 pipeline.

Install the appropriate PaddlePaddle build for the target system, then:

pip install -U "paddleocr[doc-parser]>=3.6.0" huggingface_hub

from pathlib import Path
from huggingface_hub import snapshot_download
from paddleocr import PaddleOCRVL

model_dir = snapshot_download(repo_id="VietAlphaLabs/SenOCR-Vi")

output_dir = Path("./output")
output_dir.mkdir(parents=True, exist_ok=True)

pipeline = PaddleOCRVL(
    pipeline_version="v1.6",
    vl_rec_model_dir=model_dir,
)

output = pipeline.predict("document.png")

for result in output:
    result.print()
    result.save_to_json(save_path=output_dir)
    result.save_to_markdown(save_path=output_dir)

PaddleOCR-VL also supports PDF input, layout analysis, document restructuring, and optimized serving backends. See the PaddleOCR-VL documentation.

Download the model

hf download VietAlphaLabs/SenOCR-Vi --local-dir SenOCR-Vi/

---

Evaluation

Vietnamese

SenOCR-Vi is evaluated on the corrected Vietnamese page population used for the MDPBench document-parsing comparison.

Model	Parameters	Vietnamese composite
chandra-ocr-2	5B	85.60
MonkeyOCRv2-B-Parsing	0.7B	83.20
Claude-Sonnet-4.6	Undisclosed	83.10
SenOCR-Vi	0.959B	82.83
ChatGPT-5.2-2025-12-11	Undisclosed	82.10
PaddleOCR-VL-1.6	~0.9B	80.90

External model scores are taken from the MDPBench official leaderboard. Public parameter counts are shown where available; Claude-Sonnet-4.6 and ChatGPT-5.2 do not disclose model size.

At 0.959B parameters, SenOCR-Vi is 0.27 points behind Claude-Sonnet-4.6, 0.73 points ahead of ChatGPT-5.2, and 1.93 points ahead of PaddleOCR-VL-1.6 on Vietnamese. chandra-ocr-2 scores 2.77 points higher at 5B parameters, about 5.2 times SenOCR-Vi's parameter count.

Text recognition

On the 160-page Vietnamese slice:

Metric	SenOCR-Vi
Page-level text_block Edit_dist	0.13305
1 - Edit_dist	86.7%

The 86.7% figure is a Vietnamese text-recognition score under 1 - Edit_dist; the 82.83 document composite also includes structured document elements.

Document conditions

On the controlled English/Vietnamese/Simplified Chinese population:

Condition	Composite
Digital documents	91.1
Photographed documents	80.8

Multilingual

SenOCR-Vi remains usable outside Vietnamese, although multilingual aggregate performance is not the primary optimization target.

Language	Composite
English	82.08
Vietnamese	82.83
Simplified Chinese	84.03
EN/VI/ZH macro	82.98

For comparison, PaddleOCR-VL-1.6 records an EN/VI/ZH macro of 83.43 from the corresponding MDPBench language columns.

Benchmark note

SenOCR-Vi scores are re-aggregated over the actual evaluation-page population.

The original project scorer emitted three additional non-page rows because of a documented key-parsing defect. All three affected the Vietnamese population. Removing those phantom rows gives the reported 82.83 Vietnamese composite.

The 82.98 EN/VI/ZH macro is a three-language summary only. It is not the full MDPBench overall score.

---

Training

SenOCR-Vi was initialized as a fresh decoder-only LoRA fine-tune of PaddleOCR-VL-1.6.

Setting	Value
Base model	PaddlePaddle/PaddleOCR-VL-1.6
Total parameters	958,588,736
Trainable parameters	12,091,392
Trainable share	1.2614%
LoRA rank	32
LoRA alpha	64
LoRA scaling	2.0
Decoder projections	126
Training records	42,254
Target tokens per pass	1,171,860
Effective corpus passes	3.000521
Target-token exposures	~3.516M
Optimizer steps	1,981
Effective batch size	64
Maximum sequence length	4,096
Peak learning rate	1e-4
Minimum learning rate	1e-5
Warmup	3%
Weight decay	0.01
Gradient clipping	1.0
Training precision	BF16
Final reported train loss	0.4991
Hardware	1 x NVIDIA A10G
Runtime	7,266.56 s, ~2 h 01 m

The vision encoder, vision-language aligner/projector, embeddings, and LM head remained frozen.

LoRA was applied to seven projections in each of 18 decoder layers:

• q_proj
• k_proj
• v_proj
• o_proj
• gate_proj
• up_proj
• down_proj

The final adapter was merged into the base weights in FP32. Merge-equivalence validation produced 128/128 exact decoded matches between base-plus-adapter inference and the merged model.

Data

The training corpus contains 42,254 OCR records and 1,171,860 supervised target tokens per corpus pass.

Corpus lane	Records	Target tokens
Vietnamese handwriting (Viet-Handwriting-OCR-v2)	23,046	456,066
VinText	10,174	32,531
Vietnamese text corpus	1,539	491,846
General OCR replay	4,687	22,594
Private archival corpus	2,808	168,823
Total	42,254	1,171,860

The corpus is Vietnamese-dominant, with handwriting, scene/document text, longer-form printed Vietnamese, general OCR replay, and restricted archival material. The Vietnamese handwriting lane is sourced from 5CD-AI/Viet-Handwriting-OCR-v2.

Restricted archival source material is not redistributed with SenOCR-Vi. Public datasets retain their original licensing terms.

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Limitations

SenOCR-Vi is optimized primarily for Vietnamese text recognition.

Structured elements remain more difficult than ordinary text on the controlled evaluation population:

Component	Score
Text	86.5% under 1 - NED
Table	70.7 TEDS
Formula	72.5 CDM

The model is therefore not positioned as a table- or formula-specialized OCR system.

Other difficult cases include:

• complex or irregular tables;
• formula-heavy scientific pages;
• dense multi-column layouts;
• highly colorful textbooks and magazines;
• severe image degradation or unusual page geometry.

No controlled production benchmark has yet established pages per second, optimized BF16 peak VRAM, or latency relative to other OCR systems.

For legal, financial, historical, or otherwise high-stakes transcription, human review is recommended.

License

SenOCR-Vi is released under the Apache License 2.0.

The model is built on PaddleOCR-VL-1.6, which is also distributed under Apache 2.0.

Dataset licenses vary by source. Restricted archival material is not included in the release.

Citation

@misc{vietalphalab2026senocrvi,
      title={SenOCR-Vi: A Vietnamese-Specialized Document OCR Model},
      author={VietAlpha Lab},
      year={2026},
      publisher={Hugging Face},
      url={https://huggingface.co/VietAlphaLabs/SenOCR-Vi},
}

References

• PaddleOCR-VL-1.6
• PaddleOCR-VL-1.6 documentation
• PaddleOCR-VL pipeline documentation
• MDPBench
• 5CD-AI/Viet-Handwriting-OCR-v2
• Khang T. Doan, Bao G. Huynh, Dung T. Hoang, Thuc D. Pham, Nhat H. Pham, Quan T. M. Nguyen, Bang Q. Vo, and Suong N. Hoang. Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese. arXiv:2408.12480, 2024.