MonkeyOCRv2: A Visual-Text Foundation Model for Document AI

MonkeyOCRv2: a document-native vision encoder, pretraining corpus, and downstream document AI models.

Abstract

Mainstream visual encoders are pretrained on natural images and cannot be effectively applied to document images without document-oriented adaptation, as dense text and fine-grained character strokes demand character-level visual perception. We present MonkeyOCRv2, a visual-text pretrained model for document AI. First, we construct MonkeyDoc v2, to our knowledge the largest document-image pretraining corpus, comprising 113 million images spanning 17 languages. Second, we propose a pretraining strategy that jointly learns image-to-text generation and pixel-level document reconstruction: the former aligns visual representations with textual content, while the latter preserves character strokes and layout details. Extensive experiments are conducted on five representative document analysis tasks — text recognition, formula recognition, text detection, document tampering detection, and overlapping text segmentation — where replacing the original encoders with MonkeyOCRv2 consistently improves performance. Finally, kept frozen and paired with a lightweight language model, it yields a 0.7B document parsing model that sets a new open-source state-of-the-art on MDPBench, surpassing the previous best 3B model by 2.8% absolute with a vision encoder roughly 11× smaller. The frozen encoder also powers a document understanding model that outperforms counterparts built on CLIP, DINO, and SAM across eight benchmarks under identical training settings.

113M

Pretraining Images

17

Languages

0.7B

Parsing Model

#1

MDPBench Open-source

News

2026.08.17 ⚡ Released training and evaluation instructions for Recognition and Overlapping Text Segmentation.

2026.07.24 ⚡ Released MonkeyOCRv2-B-Parsing-DFlash, enabling vLLM serving with DFlash for up to 2× faster inference.

2026.07.22 🏆 MonkeyOCRv2-B-Parsing ranks #1 among open-source models on the official MDPBench Leaderboard (83.3 overall across 17 languages).

2026.07.21 📦 Released MonkeyDoc v2, an open multilingual corpus for document-oriented pretraining.

2026.07.14 🚀 Released MonkeyOCRv2: vision encoder, MonkeyOCRv2-Parsing for multilingual document parsing, and MonkeyOCRv2-Und for efficient document understanding.

Multilingual Parsing Examples

Robust document parsing on real-world photographed documents — under challenging conditions such as underexposure, blur, wrinkles, bends and shadows — across 17 languages. Select an example to compare the captured photo (left) with the MonkeyOCRv2-B-Parsing output (right); detected image blocks are rendered inline in the parsing result.

Input photo

ZH Chinese (Simplified)

Results (.zip)

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ZH-T Chinese (Traditional)

Results (.zip)

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EN English

Results (.zip)

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AR Arabic

Results (.zip)

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DE German

Results (.zip)

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ES Spanish

Results (.zip)

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FR French

Results (.zip)

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HI Hindi

Results (.zip)

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ID Indonesian

Results (.zip)

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IT Italian

Results (.zip)

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JP Japanese

Results (.zip)

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KO Korean

Results (.zip)

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PT Portuguese

Results (.zip)

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RU Russian

Results (.zip)

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TH Thai

Results (.zip)

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VI Vietnamese

Results (.zip)

Text Detection Examples

Arbitrary-shaped scene text detection with the MonkeyOCRv2-AS encoder — accurate polygon localization of curved, vertical and artistic text in the wild.

Overlapping Text Segmentation Examples

Overlapping text segmentation with the MonkeyOCRv2-AS encoder on the MOT dataset — disentangling severely overlapping text instances (distinct colors denote different text layers and overlapping regions).

Model Zoo

1. Vision Encoder

Model	Backbone	Params	Pretraining Resolution	Applicable Tasks	Checkpoint Link
MonkeyOCRv2-S	ViT-S	28M	12802828	Recognition / Parsing / Understanding	🤗HuggingFace 🤖ModelScope
MonkeyOCRv2-B	ViT-B	113M	12802828	Recognition / Parsing / Understanding	🤗HuggingFace 🤖ModelScope
MonkeyOCRv2-AS	ViTAEv2-S	21M	17603232	Detection / Segmentation	🤗HuggingFace 🤖ModelScope

2. Document Parsing Model (results on MDPBench)

Model	Link	Total Params	ViT	LLM	All	Digit.	Photo.	Latin Avg.	DE	EN	ES	FR	ID	IT	NL	PT	VI	Non-Latin Avg.	AR	HI	JP	KO	RU	TH	ZH	ZH-T
MonkeyOCRv2-S-Parsing	HuggingFace ModelScope	0.6B	0.03B	0.6B	82.5	87.9	80.7	83.2	87.3	83.6	76.8	73.6	85.4	87.2	85.5	87.4	81.9	81.7	91.2	87.1	69.9	88.7	78.0	79.8	84.4	74.7
MonkeyOCRv2-B-Parsing	HuggingFace ModelScope	0.7B	0.1B	0.6B	83.3	88.1	81.7	84.2	87.7	84.5	75.2	78.4	86.5	88.6	86.1	87.9	83.2	82.1	90.7	87.2	71.9	87.6	80.1	80.8	83.6	75.3

DFlash-accelerated checkpoint for 2× faster inference: MonkeyOCRv2-B-Parsing-DFlash.

3. Document Understanding Model

Model	Link	Total Params	Overall	DocVQA	InfoVQA	DF	KLC	WTQ	ChartQA	DT-VQA	OCRBench
MonkeyOCRv2-S-Und	HuggingFace ModelScope	1.7B	55.9	79.3	44.5	65.1	37.6	43.0	62.0	63.1	52.2
MonkeyOCRv2-B-Und	HuggingFace ModelScope	1.8B	57.2	79.3	46.3	65.8	38.2	43.2	62.0	64.3	58.1

Document parsing results on MDPBench, a comprehensive multilingual benchmark for real-world document parsing. Bold: best among open-source; underline: second best.

Model	Total Params	ViT	LLM	All	Digit.	Photo.	Latin Avg.	DE	EN	ES	FR	ID	IT	NL	PT	VI	Non-Latin Avg.	AR	HI	JP	KO	RU	TH	ZH	ZH-T
Closed-source VLMs
ChatGPT-5.2-2025-12-11	-	-	-	68.6	85.6	63.0	75.2	70.8	79.4	71.4	60.0	77.7	78.5	71.6	85.0	82.1	61.1	64.9	63.4	55.8	65.4	60.7	63.8	56.3	58.7
Claude-Sonnet-4.6	-	-	-	73.1	85.0	69.3	79.2	79.8	80.6	72.8	66.5	82.3	83.3	76.7	88.0	83.1	66.2	67.8	71.7	63.4	64.3	70.8	65.2	61.3	65.1
Doubao-2.0-pro	-	-	-	74.2	78.9	72.8	75.7	82.8	74.4	69.0	70.0	73.3	82.0	69.9	83.4	76.5	72.5	81.3	75.7	65.8	74.7	63.3	71.9	71.9	75.2
Gemini-3-pro	-	-	-	86.4	90.4	85.1	88.4	91.2	90.6	83.4	82.7	91.5	91.6	87.7	91.4	85.9	84.1	89.4	90.4	74.8	85.5	84.9	80.6	85.1	82.1
Open-source VLMs
InternVL-3.5-8B	8.3B	0.3B	8B	42.7	59.7	37.0	53.4	39.8	64.2	47.5	42.7	53.8	60.6	52.2	63.2	57.0	30.6	8.2	9.0	45.6	30.3	26.1	10.8	55.3	59.3
MinerU-2.5	1.2B	0.7B	0.5B	46.3	61.9	40.8	63.0	68.8	78.4	54.7	57.3	67.5	75.2	60.4	58.8	46.0	27.4	1.3	9.0	39.1	14.7	8.6	11.3	72.9	62.2
DeepSeek-OCR	3.4B	0.4B	3B	51.8	80.7	42.2	54.5	55.0	58.3	44.1	43.2	60.9	69.3	52.4	53.0	54.1	48.9	56.9	52.2	49.1	28.2	36.2	49.4	59.7	59.2
MonkeyOCR-pro-3B	3.7B	0.7B	3B	52.2	68.0	47.0	65.1	71.7	77.9	55.9	62.1	66.2	74.5	66.3	71.1	40.2	37.6	4.6	4.2	55.2	60.5	42.6	9.1	72.2	52.4
Nanonets-OCR-s	4.7B	0.7B	4B	63.7	78.8	58.7	71.3	75.1	78.5	61.2	62.5	70.3	81.0	69.6	75.9	67.5	55.0	59.5	61.8	55.9	51.2	43.5	39.5	67.4	61.5
Nanonets-OCR2-3B	3.7B	0.7B	3B	64.2	79.2	59.3	71.4	76.7	76.4	61.8	66.1	68.4	78.5	74.1	74.2	66.0	56.2	60.2	59.2	52.1	54.7	45.5	44.6	68.3	65.1
Qwen3.5-Instruct-9B	9.7B	0.7B	9B	65.7	74.8	62.7	72.5	72.8	72.0	72.0	64.4	66.2	77.6	74.5	79.1	74.0	58.2	53.4	56.2	55.7	60.3	54.7	56.7	60.8	67.5
GLM-OCR	0.9B	0.4B	0.5B	67.3	77.9	63.7	78.7	82.7	84.5	75.8	76.2	79.7	82.8	80.2	77.4	69.2	54.3	21.7	39.6	65.5	61.2	64.2	27.4	78.5	76.7
Qwen3-VL-Instruct-8B	8.3B	0.3B	8B	68.3	78.4	65.0	73.6	73.7	71.4	69.3	66.2	68.5	79.1	78.3	82.2	73.4	62.5	63.1	58.4	59.9	61.9	57.9	62.0	62.6	73.8
HunyuanOCR	1B	0.4B	0.6B	68.3	80.2	64.3	72.4	75.0	73.1	63.0	66.1	69.9	80.3	61.4	81.9	80.6	63.7	68.3	73.1	55.6	68.9	52.2	60.7	66.8	64.2
PaddleOCR-VL	0.9B	0.6B	0.3B	69.6	87.6	63.6	72.1	78.2	79.3	62.9	66.0	77.4	78.4	67.9	72.0	66.6	66.7	65.8	68.4	59.9	77.8	56.9	57.8	78.2	68.5
olmOCR2	7.7B	0.7B	7B	70.4	79.9	67.2	76.7	75.7	77.3	72.5	68.9	70.6	81.0	72.0	88.0	84.0	63.3	59.0	60.8	59.4	70.6	65.8	59.2	68.6	63.4
MinerU-2.5-Pro	1.2B	0.7B	0.5B	71.0	86.2	66.1	74.6	78.3	79.5	63.4	67.4	78.0	79.7	72.1	78.6	74.2	67.0	56.6	72.2	59.1	77.6	62.6	61.8	76.5	69.7
PaddleOCR-VL-1.6	0.9B	0.6B	0.3B	75.0	82.8	72.6	78.0	84.1	79.7	69.2	74.8	81.6	82.0	74.7	76.4	79.3	71.6	69.4	65.6	68.7	82.5	70.7	62.3	78.0	75.7
HunyuanOCR-1.5	1B	0.4B	0.6B	76.8	86.2	73.6	79.7	79.6	80.4	74.2	70.0	81.5	84.5	78.4	86.4	82.4	73.5	71.8	71.6	65.5	75.7	67.4	77.7	80.8	77.2
Kimi-K2.5	1T	0.4B	1T	77.5	85.0	75.0	81.6	85.9	86.2	72.7	71.0	80.6	86.6	77.4	87.6	86.2	72.9	75.8	74.5	72.5	70.9	61.8	67.0	81.7	78.6
PaddleOCR-VL-1.5	0.9B	0.6B	0.3B	78.3	87.4	75.2	81.2	84.8	83.0	75.7	78.1	83.9	85.2	80.6	80.2	78.9	74.9	71.3	67.7	69.5	86.0	76.0	68.4	84.8	75.7
chandra-ocr-2	5.3B	0.5B	4.8B	79.7	87.8	77.1	82.7	86.6	86.5	69.7	70.3	84.6	87.4	82.7	90.7	85.6	76.4	78.2	81.1	68.8	80.3	74.0	78.5	73.8	76.3
dots.mocr	3B	1.2B	1.8B	80.5	90.5	77.2	81.7	82.6	87.4	71.3	70.1	84.5	89.3	83.2	86.8	79.9	79.2	83.3	83.6	75.0	78.7	71.2	77.9	84.6	79.6
MonkeyOCRv2-S-Parsing 🤗	0.6B	0.03B	0.6B	82.5	87.9	80.7	83.2	87.3	83.6	76.8	73.6	85.4	87.2	85.5	87.4	81.9	81.7	91.2	87.1	69.9	88.7	78.0	79.8	84.4	74.7
MonkeyOCRv2-B-Parsing 🤗	0.7B	0.1B	0.6B	83.3	88.1	81.7	84.2	87.7	84.5	75.2	78.4	86.5	88.6	86.1	87.9	83.2	82.1	90.7	87.2	71.9	87.6	80.1	80.8	83.6	75.3

Document understanding performance comparison across different vision foundation models. The evaluation benchmarks are selected following TextMonkey and DT-VQA.

Model	Params	Overall	DocVQA	InfoVQA	DF	KLC	WTQ	ChartQA	DT-VQA	OCRBench
CLIP-B	86M	16.0	20.1	24.2	2.3	13.8	12.8	22.2	22.3	10.6
SigLIP2-B	93M	24.9	27.0	23.5	3.1	16.7	17.4	35.0	41.5	35.1
RADIOv2.5-B	98M	37.5	60.3	31.2	29.9	30.4	29.7	51.1	44.2	23.1
OpenVision-B	87M	44.0	63.3	30.7	19.8	33.1	31.1	58.3	62.6	52.9
DINOv3-B	86M	16.1	26.5	20.8	5.6	13.2	14.0	28.9	15.8	3.9
SAM-B	90M	25.2	37.8	22.2	4.7	17.5	17.6	46.5	33.3	21.9
SAM2-B	69M	22.3	32.5	21.9	2.7	15.8	16.6	40.2	30.3	18.4
oCLIP	24M	12.4	14.8	19.5	1.4	7.4	11.4	17.9	19.2	7.4
DiT	86M	8.9	11.3	20.9	0.9	5.2	9.9	12.0	9.2	1.9
MonkeyOCRv2-S* 🤗Link	28M	55.9	79.3	44.5	65.1	37.6	43.0	62.0	63.1	52.2
MonkeyOCRv2-B* 🤗Link	113M	57.2	79.3	46.3	65.8	38.2	43.2	62.0	64.3	58.1

1. Text Recognition

Text recognition results on Common Benchmarks, Union14M-Benchmark, OST, and Chinese Benchmarks. We follow the training and evaluation protocols of OpenOCR.

Model	Overall	Union14M-Benchmark								Chinese Benchmarks					Occlusion SceneText
Model	Overall	Avg	Artistic	Contextless	Curve	General	Multi-Oriented	Multi-Words	Saliency	Avg	Scene	Web	Document	Handwriting	Occlusion SceneText
ABINet	73.7	75.7	71.7	74.7	80.4	79.8	69.0	76.8	77.6	70.3	66.6	63.2	98.2	53.1	75.0
MAERec	81.6	85.2	79.0	84.2	89.1	84.6	87.1	85.9	86.3	83.1	84.4	83.0	99.5	65.6	76.4
CPPD	80.4	81.9	76.5	82.9	86.2	83.5	78.7	81.9	83.5	81.7	82.7	82.4	99.4	62.3	79.6
IGTR-AR	81.0	84.9	77.0	82.4	90.4	84.4	91.2	84.0	84.7	81.7	82.0	81.7	99.5	63.8	76.3
SMTR	80.4	85.0	76.8	83.9	89.1	83.7	87.7	89.3	84.6	82.7	83.4	83.0	99.3	65.1	73.5
SVTRv2	83.1	86.1	79.3	86.1	90.6	85.1	89.0	86.7	86.2	83.3	83.5	83.3	99.5	67.0	80.0

CRNN (ResNet)	58.7	49.2	51.2	62.3	48.1	68.2	13.0	60.4	41.4	68.8	63.8	68.2	97.0	46.1	58.0
CRNN (MonkeyOCRv2-S)	67.3	65.2	63.7	73.0	71.1	74.5	28.6	72.1	73.4	74.2	73.0	74.9	96.9	51.8	62.4
PARSeq (ViT)	82.2	84.3	76.5	83.4	87.6	84.9	88.8	84.3	84.4	82.4	84.2	82.8	99.5	63.0	79.9
PARSeq (MonkeyOCRv2-S)	84.3	87.6	78.6	86.4	92.1	85.4	93.9	88.7	87.7	83.7	84.6	83.2	99.5	67.3	81.5

2. Formula Recognition

Formula recognition results on OmniDocBench 1.6, MathWriting, and UniMER-Test.

Model	Params	Overall		OmniDocBench 1.6		MathWriting		SPE		CPE		HWE		SCE
Model	Params	CDM	ExpRate	CDM	ExpRate	CDM	ExpRate	CDM	ExpRate	CDM	ExpRate	CDM	ExpRate	CDM	ExpRate
Pix2tex	25.5M	53.8	23.3	69.4	27.0	0.4	0.0	96.2	72.4	64.9	7.1	24.5	0.6	67.6	32.8
Texify	312M	67.3	40.4	76.5	46.4	26.6	2.0	98.5	91.0	70.4	28.2	52.7	23.6	79.3	51.3
UniMERNet-B	325M	89.5	64.5	90.4	59.5	63.8	12.3	99.1	93.3	96.0	80.5	94.0	64.3	93.7	77.0
UniMERNet-S	202M	89.8	64.0	90.1	59.1	65.9	12.7	99.1	93.4	95.9	77.7	93.7	63.9	94.1	76.9

UniMERNet-T (Swin)	107M	89.4	61.8	89.9	57.2	65.6	12.9	99.1	92.3	94.9	69.9	93.3	61.9	93.8	76.6
UniMERNet-T (MonkeyOCRv2-S)	110M	90.9	66.4	90.8	61.1	70.8	16.2	99.2	93.8	96.1	79.2	94.3	69.5	94.0	78.6

1. Text Detection

Text detection results on Total-Text, CTW1500, ICDAR2015 and ArT. We follow the training and evaluation protocols of MMOCR and DPText-DETR.

2. Document Tampering Detection

Document tampering detection results on the DocTamper benchmark.

Method	Params	Overall		DocTamper-Test				DocTamper-FCD				DocTamper-SCD
Method	Params	IoU	F	IoU	P	R	F	IoU	P	R	F	IoU	P	R	F
PSCC-Net	5M	13.7	31.3	17.0	25.0	83.0	39.0	13.0	19.0	82.0	30.0	11.0	15.0	83.0	25.0
UperNet	67M	49.3	54.0	70.0	66.0	60.0	62.0	30.0	57.0	35.0	43.0	48.0	57.0	58.0	57.0
CAT-Net	114M	67.3	71.0	78.0	75.0	69.0	72.0	66.0	85.0	70.0	76.0	58.0	65.0	65.0	65.0
Swin-UPer	81M	66.7	71.7	79.0	75.0	72.0	73.0	64.0	80.0	70.0	75.0	57.0	66.0	68.0	67.0
SegFormer	85M	70.3	74.0	81.0	77.0	74.0	75.0	69.0	82.0	74.0	78.0	61.0	68.0	70.0	69.0
Mask2Former	69M	69.7	78.0	84.0	82.0	83.0	82.0	66.0	81.0	75.0	78.0	59.0	70.0	79.0	74.0
ConvNext	122M	69.7	75.3	84.0	81.0	78.0	79.0	62.0	76.0	71.0	74.0	63.0	71.0	74.0	73.0
ConvNextV2	121M	72.7	77.7	86.0	82.0	79.0	81.0	65.0	79.0	75.0	77.0	67.0	74.0	76.0	75.0
InternImage	128M	73.3	77.7	84.0	81.0	77.0	79.0	72.0	83.0	79.0	81.0	64.0	73.0	74.0	73.0
ASC-Former	80M	68.2	80.8	81.5	91.8	87.8	89.8	61.3	74.9	77.1	76.0	61.9	78.0	75.0	76.5
DTD	66M	77.0	79.7	84.0	81.0	77.0	79.0	79.0	88.0	82.0	85.0	68.0	75.0	76.0	75.0

FFDN* (ViTAEv2)	69M	70.7	82.7	69.4	76.2	88.7	82.0	79.0	92.5	84.4	88.3	63.6	79.1	76.5	77.8
FFDN (MonkeyOCRv2-AS)	71M	78.2	87.5	87.4	94.8	91.8	93.3	79.9	90.4	87.4	88.9	67.2	81.0	79.8	80.4

* denotes models trained with the ViTAEv2 pretrained by DeepSolo.

3. Overlapping Text Segmentation

Overlapping text segmentation results on the MOT dataset.

Model	mIoU_Text	IoU_Occ	IoU_Occd	IoU_Ov
Unet	62.2	80.2	65.7	40.7
Deeplab v3	67.9	83.2	71.2	49.3
OCRNet	65.8	81.0	68.5	47.8
Segformer	69.0	83.6	74.1	49.3
MaskFormer	68.4	83.5	70.3	51.4
TexRNet	68.9	84.2	73.2	49.3
EAFormer	69.1	83.8	74.2	50.5
WASNet	70.8	84.8	74.4	53.1

Mask2Former (ResNet)	70.3	84.7	73.3	52.8
Mask2Former (MonkeyOCRv2-AS)	76.6	88.6	83.4	57.7
MOTS (ResNet)	72.6	85.2	77.5	54.9
MOTS (MonkeyOCRv2-AS)	76.9	88.6	82.6	59.4

MonkeyDoc v2

MonkeyDoc v2 is currently the largest document image pre-training image-text pair dataset, comprising 113 million document images across 17 languages. So far, 52M synthetic samples and 41M real-world samples have been released, with more being organized progressively. Annotations are produced by a multi-expert labeling toolchain: structure detection & reading order by dots.mocr, content recognition by three expert models (dots.mocr, PaddleOCR-VL, Qwen3-VL), block-level agreement filtering, and page-level quality control verified by Qwen3 / Qwen3-VL.

Download MonkeyDoc v2

Citation

If you use any part of this release — the encoders, MonkeyOCRv2-Parsing, MonkeyOCRv2-Und, MDPBench, or MonkeyDoc v2 — please cite:

@article{liu2026monkeyocrv2,
  title   = {MonkeyOCRv2: A Visual-Text Foundation Model for Document AI},
  author  = {Liu, Yuliang and Li, Zhang and Zhang, Ziyang and Zhang, Shuo and
             Liu, Qiang and Song, Jiajun and Guo, Zidun and Wang, Xinhan and
             Zheng, Handong and Liu, Yang and Luo, Dongliang and Ma, Zhiyin and
             Zhang, Jiarui and Bai, Xiang},
  journal = {arXiv preprint arXiv:2607.11562},
  year    = {2026}
}