MiniCPM-OCR

MiniCPM-V Multimodal Series

MiniCPM-OCR is a family of models for efficient document parsing and text recognition, releasing a parsing model and a System-1 decision model together for the first time:

• MiniCPM-OCR 1.0 (parsing model): With just 1.3B parameters, it achieves SOTA results on both Real5-OmniDocBench (93.68) and OCRBench v2 (54.7), supporting efficient parsing of scans, photographs, curved, skewed and recaptured documents.
• MiniCPM-OCR Jev (decision model): It assesses the quality of text, formula and table parsing results and returns calibrated probabilities, without parsing text itself (JevOCRBench overall: 93.5), helping developers evaluate parsing quality and identify results that need closer review.

Model List

Checkpoint	Parameters	Architecture	Task
MiniCPM-OCR-1.0	1.3B	SigLIP + Qwen3.5, trained from MiniCPM-V 4.6	Document parsing, recognition and field extraction
MiniCPM-OCR-Jev	2B	Qwen3.5-2B + binary classification head	Quality assessment for text, formulas and tables

Core Features

MiniCPM-OCR 1.0

• Document recognition: Chinese and English text, tables, formulas, charts, seals and key information extraction.
• Complete-document parsing: The minicpmvocr SDK combines PP-DocLayoutV3 layout detection with the recognition service. It assembles Markdown in reading order, converts OTSL tables to HTML and preserves LaTeX formulas.
• DSpark acceleration: Speculative decoding uses a matching draft model from the same release; standard decoding is also available.

MiniCPM-OCR Jev

• Region-level decisions: The model takes the original region image, candidate OCR text and its rendered image. A renderer-enabled service only requires the original image, candidate text and the correct category.
• Review routing: Calibrate acceptance thresholds on your labelled business samples and route lower-scoring regions for review.
• Candidate selection: Score outputs from multiple OCR models using the same crop boundaries and select the highest-scoring candidate. Regions with no acceptable candidate can still go to review.

Evaluation

Model	Benchmark	Reported score
MiniCPM-OCR 1.0	Real5-OmniDocBench	93.68
MiniCPM-OCR 1.0	OCRBench v2	54.7
MiniCPM-OCR Jev	JevOCRBench (overall)	93.5

The parsing throughput reference is about 1.2 pages/s on an RTX 5090, using 1,651 OmniDocBench v1.6 images, image-level concurrency 1 and the accelerated configuration. Regions within a page can still run concurrently. Hardware, page content and concurrency affect throughput.

Quick Start

Start with the published Linux x86_64 / NVIDIA recipe, verified on RTX 5090 32 GB. It uses Python 3.12 and driver ≥580 (CUDA 13); reserve at least 40 GB disk. The SDK and recognition backend run in separate environments.

vLLM

Install the matching backend using the repository script:

Bash

git clone https://github.com/OpenBMB/MiniCPM-OCR.git
cd MiniCPM-OCR
apt-get update
apt-get install -y software-properties-common
add-apt-repository -y ppa:ubuntu-toolchain-r/test
apt-get update
apt-get install -y gcc-13 g++-13 libdw-dev libelf-dev
bash scripts/install.sh vllm

Open another terminal at the repository root and launch the service:

Bash

source .venv-vllm/bin/activate
export CC=gcc-13 CXX=g++-13
export CUDA_HOME="$(python -c 'import sysconfig; print(sysconfig.get_path("purelib") + "/nvidia/cu13")')"
export PATH="$CUDA_HOME/bin:$PATH"

vllm serve openbmb/MiniCPM-OCR-1.0 \
  --served-model-name minicpm-ocr \
  --host 127.0.0.1 \
  --port 18499 \
  --max-model-len 40960 \
  --max-num-batched-tokens 40960 \
  --max-num-seqs 32 \
  --limit-mm-per-prompt '{"image":36,"video":0}' \
  --attention-backend FLASH_ATTN \
  --mm-encoder-attn-backend FLASH_ATTN \
  --mm-processor-kwargs '{"downsample_mode":"4x"}' \
  --gpu-memory-utilization 0.85 \
  --trust-request-mm-kwargs \
  --speculative-config '{
    "method": "dspark",
    "model": "openbmb/MiniCPM-OCR-1.0-DSpark",
    "num_speculative_tokens": 15,
    "enable_adaptive_verification": false
  }' \
  --default-chat-template-kwargs '{"enable_thinking":false}'

Check that the service returns minicpm-ocr:

Bash

curl -fsS http://127.0.0.1:18499/v1/models

The command enables DSpark with a matching draft model. For standard decoding, remove the entire --speculative-config argument and JSON value. Complete backend setup and troubleshooting are in Deployment.

CLI

Keep the recognition service running. At the repository root, install the SDK in its own environment and parse doc.pdf:

Bash

pip install uv
uv venv .minicpm-ocr --python 3.12 --seed
source .minicpm-ocr/bin/activate
python -m pip install "minicpm-ocr[all]==1.0.2"
python -m pip install "paddlepaddle-gpu==3.3.0" \
  -i https://www.paddlepaddle.org.cn/packages/stable/cu129/

minicpmvocr parse \
  --device cuda:0 \
  --server-url http://127.0.0.1:18499 \
  --model minicpm-ocr \
  --input doc.pdf \
  --output doc.md

The SDK downloads missing layout weights automatically. Check doc.md for the parsing result. For region recognition and Jev scoring, see Inference and Jev inference.

Related Resources

Topic	Link
Code Repository	OpenBMB/MiniCPM-OCR
Online demo	MiniCPM-OCR Demo
API Access	API
Official Documentation	SDK
Evaluation	OCR 1.0 / Jev / Reproduction / JevOCRBench
Fine-tuning	MiniCPM-OCR
Deployment	MiniCPM-OCR Deployment
Inference	MiniCPM-OCR Inference
Quantization	MiniCPM-OCR Quantization
Model Weights	OCR 1.0 / Jev