Model reference · open weights

TeleOCR

Name: TeleOCR
Brand: AxForge

Available as managed deployment LLMs StarDoc-AI Vision + text 1 variants 16k dl/mo

TeleOCR is an open-weight language model from StarDoc-AI. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

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Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Released by	StarDoc-AI
Type	Language models
Task	Vision + text
Parameters (lead)	1.4B
Context	125k tokens
Runs with	transformers
Released	2026-08-14
Popularity	16k downloads / month
Licence	Open weights

About

What TeleOCR is

TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents

Read the full model card

🔥 News

2026/09/10 - We have renamed NaviDC-OCR to TeleOCR, and all subsequent model iterations will be developed and released under the TeleOCR version.
2026/09/01 - We noticed that EMNLP 2026 is hosting the Dr.DocBench Challenge, a document parsing competition. We evaluated NaviDC-OCR with its native weights, achieving better results than MinerU 2.5 Pro and PaddleOCR-VL 1.6. Detailed results are shown below dr.docbench-challenge. We welcome the use of NaviDC‑OCR for competitions. Going forward, we will continue to deliver competitive parsing models for the community.
2026/08/29 — Thanks to Nandraj for the GGUF conversion and llama.cpp support! 🔗 NaviDC-OCR-GGUF
2026/08/17 — NaviDC-OCR model weights and technical report have been released.

📖 Introduction

TeleOCR is a lightweight (~1.2B parameters), open-source Vision-Language Model designed specifically for document parsing.

Unlike existing methods that mainly target either digital documents or camera-captured documents, TeleOCR unifies both scenarios within a single framework.

Compared with previous document parsing models, TeleOCR introduces

Multi-node Consensus Voting (MCV) for automatic pseudo-label generation
Geometry-aware document modeling for camera-captured documents
Curvature-Guided Douglas-Peucker Sampling (CGDP)
Image-to-image self-verification for automatic data refinement
Progressive four-stage training pipeline
Content-Structure Decoupled Learning for tables and formulas

These techniques enable TeleOCR to achieve state-of-the-art performance on both digital and camera-captured document benchmarks while remaining lightweight enough for practical deployment.

📊 Experimental Results

TeleOCR achieves state-of-the-art performance on multiple public document parsing benchmarks.

Layout Visualization of Distorted Documents

To evaluate the model's ability to understand complex document deformations, we conduct a visual evaluation on the public dewarping datasets DocUNet and DIR300, with representative results shown in Figure. TeleOCR directly performs layout and content parsing on distorted documents without dewarping preprocessing or a dedicated rectification model, demonstrating robust parsing under complex geometric deformations.

Dr.DocBench Challenge

模型	overall ↑	Text edit ↓	formula cdm ↑	Table teds ↑	order edit ↓
Specialized VLMs
TeleOCR	67.96	0.1903	0.02	64.97	0.398
Mineru 2.5 pro	62.26	0.3402	0.04	67.75	0.356
OvisOCR2	59.25	0.3883	0.00	61.59	0.3791
PaddleOCRvl 1.6	55.11	0.4364	0.21	51.34	0.412

OmniDocBench v1.6

Model Type	Methods	Param	Overall ↑	Text Edit ↓	Formula CDM ↑	Table TEDS ↑	Table TEDS-S ↑	Read Order Edit ↓
Specialized VLMs	TeleOCR	1.2B	96.87	0.027	96.36	97.05	98.52	0.122
	OvisOCR2	0.8B	96.58	0.025	97.53	94.76	97.16	0.111
	PaddleOCR-VL-1.6	0.9B	96.33	0.033	97.49	94.76	97.11	0.127
	MinerU2.5-Pro	1.2B	95.75	0.036	97.45	93.42	95.92	0.120
	GLM-OCR	0.9B	95.22	0.044	97.18	92.83	95.39	0.133
	PaddleOCR-VL-1.5	0.9B	94.87	0.038	96.69	91.67	94.37	0.130
	HunyuanOCR-1.5	1B	94.74	0.033	97.49	94.76	97.11	0.127
	PaddleOCR-VL	0.9B	94.11	0.040	95.70	90.65	93.74	0.135
	Youtu-Parsing	2.5B	93.68	0.044	93.45	92.02	95.00	0.116
	Logics-Parsing-v2	4B	93.27	0.041	95.47	88.42	91.98	0.137
	FireRed-OCR	2B	93.20	0.037	95.27	88.04	91.06	0.131
	MinerU2.5	1.2B	92.98	0.045	95.59	87.88	91.47	0.130
	OpenDoc-0.1B	0.1B	90.64	0.049	92.93	83.88	87.45	0.140
	dots.ocr	3B	90.50	0.048	89.12	87.18	90.58	0.138
	DeepSeek-OCR 2	3B	90.17	0.050	91.59	83.89	87.75	0.144
	HunyuanOCR	1B	89.87	0.089	87.44	91.01	93.23	0.171
	Dolphin-v2	3B	89.34	0.069	90.53	84.40	87.44	0.150
	OCRVerse	4B	88.44	0.063	89.14	82.44	86.27	0.163
	MonkeyOCR-pro-3B	3B	88.43	0.074	88.33	84.35	88.62	0.189
General VLMs	Ovis2.6-30B-A3B	30B	93.62	0.035	94.93	89.44	92.40	0.135
	Gemini 3 Pro	--	92.85	0.064	95.83	89.15	92.96	0.165
	Gemini 3 Flash	--	92.58	0.066	95.03	89.29	93.51	0.173
	Qwen3-VL-235B	235B	89.78	0.063	92.53	83.07	86.75	0.166
	GPT-5.2	--	86.52	0.114	88.00	82.95	87.93	0.193
	InternVL3.5-241B	241B	83.61	0.130	89.52	74.35	79.78	0.215

Wild_OmniDocBench

Model Type	Methods	Param	Overall ↑	Text Edit ↓	Formula CDM ↑	Table TEDS ↑	Table TEDS-S ↑	Read Order Edit ↓
Decoupled VLMs	TeleOCR	1.2B	88.53	0.1173	88.26	89.05	92.14	0.2011
	PaddleOCR-VL-1.6	0.9B	87.36	0.1369	88.42	85.76	90.14	0.2057
	MinerU2.5-Pro	1.2B	87.33	0.1362	90.15	85.46	90.12	0.2013

From the published model card. Full card on the HuggingFace links in the sidebar.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys teleocr for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (teleocr below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"teleocr","messages":[{"role":"user","content":"Hello"}]}'

Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.

Get API access

Sizes, precisions & builds

TeleOCRfits 16 GB

1.4B · BF16 · ~3.3 GB

Weights ↗

Details

Languages

zhenja