# TeleOCR URL: https://interfaze.ai/models/stardoc-aiteleocr [All models](https://interfaze.ai/models) TeleOCR by StarDoc-AI, a image-text-to-text model with OCR, object detection, multimodal capabilities. Understand and compare OCR, object detection, multimodal features, benchmarks, and capabilities. ## Comparison | Feature | TeleOCR | Interfaze | | --- | --- | --- | | Input Modalities | text, image | image, text, audio, video, document | | Native OCR | Yes | Yes | | Long Document Processing | No | Yes | | Language Support | 3 partial | 162+ | | Native Speech-to-Text | No | Yes | | Native Object Detection | Yes | Yes | | Guardrail Controls | No | Yes | | Context Input Size | 131.1K | 1M | | Tool Calling | No | Tool calling supported + built in browser, code execution and web search | ### OCR Capabilities | Feature | TeleOCR | Interfaze | | --- | --- | --- | | Text Bounding Boxes | Partial | Yes | | Confidence Scores | No | Yes | | Dense Image Processing | No | Yes | | Low Quality Images | Partial | Yes | | Handwritten Text | No | Yes | | Charts, Tables & Equations | Partial | Yes | ### Object Detection Capabilities | Feature | TeleOCR | Interfaze | | --- | --- | --- | | Object Bounding Boxes | Partial | Yes | | Object Segmentation Masks | Partial | Yes | | Confidence Scores | No | Yes | | Dense Image Processing | Yes | Yes | | Low Quality Images | Partial | Yes | | Industry-Specific | Yes | Yes | | GUI Element Detection | No | Yes | ### Scaling | Feature | TeleOCR | Interfaze | | --- | --- | --- | | Scaling | Self-hosted/Provider-hosted with quantization | Unlimited | [Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs) View model card on [Hugging Face](https://huggingface.co/StarDoc-AI/TeleOCR) [![License](https://img.shields.io/badge/License-Apache--2.0-blue.svg)](https://huggingface.co/StarDoc-AI/TeleOCR/blob/main/LICENSE) [![GitHub](https://img.shields.io/badge/GitHub-Repository-black?logo=github)](https://github.com/caipeng328/TeleOCR) [![Papers with Code: SOTA on OmniDocBench v1.6](https://paperswithcode.co/api/v1/papers/2608.12898/leaderboard-badge.svg?eval=25856&live=1)](https://paperswithcode.co/api/v1/papers/2608.12898/leaderboard-badge-link?eval=25856) [![TeleAI](https://img.shields.io/badge/webapp_on_teleai.com-blue?logo=data:image/png;base64,UklGRpICAABXRUJQVlA4WAoAAAAQAAAAFQAAGAAAQUxQSEwBAAABkGvb2rFXb5zauQCbVTrbNjt1tm3bObZt6zeObdvft4v30yVExASwkqKChaQVLH6f2vcKeHqAunMTHz9x/D9Y/+2cVtX1+dAbjLX8lck4cH1c37S4Z3DT/NznjkmlWs6lXv26sf/K0rFlmWzkwIGHOo6/qhvqaxdlN+Vnrt9Q6a8pvnPpNRqtTqPV6bUajUar/cXB/xsChfT71DISHCg+UzIjweRb5uOO/2KuLxw5duTS4w96Qf+aS89/BfBe193AUFXUFbuftx4DeHkELnsB/A0EZfPs1j0Ar099tz8D4LfbfQDsQuHmW3+Yv/pN5f0sgIcWS6Cf7dq1t2XHnu13Qc8R1/cUsOU1vq2C84sXIUm/OC6EzMXu5PiVQwghUW8prAUMM9SrLMLpvPwf/D9WnQl/2FGW67g/EWwQefw/cDnNlIg1StxXYE74AVZQOCAgAQAAkAcAnQEqFgAZAD7RVKZNqCQjojAYCAEAGglsAJ0zND+A2rdxh/l2DrrSwNZEPJb8c9uqnP90eUrVexSs2yAJSFsrsI8AAP03X4scCDMKfncvKAr9aIf33A4Lc9xfcBVOo9hotIZIL1kpgEs0hdr2CPz/BVzkPcV5X2+ZdiEn0NWI16mvKRFSI/HYHhAAT9NP3KwVp1une4gB+ernhg1Ly0KuLdqCAZXWQHJzTrrR8RNz79tgl7Aq8WHqxbOTfguubnUbMPaD4DfKY71NjLtozHqNKPSC6gxIS3UNlbGZT3m3zEus+sjwYwucztbFfyH6n4U8VoWfQf4UdhLuoylwhqzt+FoCGwH4pHiWv/pkL2Z/bt9h755P3YDljRFfAAAA&labelColor=white)](https://www.teleai.com.cn/docparse/DocumentParsing) [![arXiv](https://img.shields.io/badge/TeleOCR-Technical%20Report-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2608.12898) ## πŸ”₯ 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](https://eval.ai/web/challenges/challenge-page/2717/overview), 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** β€” Thanks to Nandraj for the GGUF conversion and llama.cpp support [πŸ”— NaviDC-OCR-GGUF](https://huggingface.co/nandraj/NaviDC-OCR-GGUF), and to the community for sharing their experience deploying NaviDC-OCR on [Ascend 910B!](https://zhuanlan.zhihu.com/p/2078516899093155893) - **2026/08/17** β€” NaviDC-OCR [model weights](https://huggingface.co/StarDoc-AI/NaviDC-OCR) and [technical report](https://arxiv.org/pdf/2608.12898) have been released. ## πŸ“– 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. ## πŸ“Š 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 | ζ¨‘εž‹ | 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](https://github.com/opendatalab/OmniDocBench) | 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](https://github.com/VirtualLUOUCAS/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 | | | GLM-OCR | 0.9B | 85.08 | 0.1514 | 89.09 | 81.31 | 85.90 | 0.2228 | | | PaddleOCR-VL-1.5 | 0.9B | 84.64 | 0.1461 | 86.72 | 81.80 | 86.52 | 0.2138 | | End-to-End VLMs | OvisOCR2 | 0.8B | 87.91 | 0.129 | 90.37 | 85.13 | 89.11 | 0.2021 | | | dots.ocr | 3B | 81.84 | 0.1483 | 85.0 | 75.32 | 80.20 | 0.2200 | | | HunyuanOCR-1.5 | 1B | 77.62 | 0.1979 | 85.12 | 67.54 | 70.67 | 0.2750 | | | Logics-Parsing-v2 | 4B | 77.10 | 0.4029 | 91.4 | 80.19 | 87.16 | 0.2355 | * * * ### [PureDocBench](https://github.com/zhihengli-casia/puredocbench/) | Model Type | Model | Clean Overall ↑ | Clean Text ↓ | Clean Formula ↑ | Clean Table ↑ | Digital Degraded Overall ↑ | Digital Degraded Text ↓ | Digital Degraded Formula ↑ | Digital Degraded Table ↑ | Real Degraded Overall ↑ | Real Degraded Text ↓ | Real Degraded Formula ↑ | Real Degraded Table ↑ | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Decoupled VLM | TeleOCR | 86.90 | 0.111 | 81.01 | 91.09 | 77.47 | 0.206 | 72.59 | 80.45 | 70.85 | 0.302 | 65.11 | 77.66 | | | DotsMOCR | 76.27 | 0.151 | 66.23 | 77.65 | 73.16 | 0.198 | 64.32 | 74.95 | 61.73 | 0.312 | 54.39 | 61.97 | | | MinerU2.5-Pro | 75.87 | 0.222 | 65.14 | 84.68 | 71.77 | 0.272 | 61.79 | 80.73 | 62.56 | 0.375 | 52.70 | 72.47 | | | YouTu-Parsing | 75.02 | 0.230 | 67.34 | 80.74 | 69.66 | 0.270 | 61.44 | 74.49 | 60.29 | 0.360 | 52.20 | 64.69 | | | PaddleOCR-VL-1.5 | 73.01 | 0.266 | 63.53 | 82.12 | 66.73 | 0.339 | 58.03 | 76.07 | 60.50 | 0.398 | 54.00 | 67.33 | | | GLM-OCR | 68.65 | 0.314 | 57.89 | 79.44 | 63.06 | 0.383 | 53.23 | 74.21 | 58.31 | 0.433 | 50.34 | 67.83 | | | Dolphin-v2 | 65.90 | 0.342 | 59.80 | 72.12 | 60.24 | 0.393 | 52.20 | 67.86 | 44.92 | 0.553 | 39.98 | 50.04 | | | MonkeyOCR-pro-3B | 62.23 | 0.346 | 48.46 | 72.83 | 57.40 | 0.397 | 45.57 | 66.32 | 46.49 | 0.511 | 38.18 | 52.43 | | End-to-End VLM | OvisOCR2 | 82.14 | 0.149 | 71.29 | 90.12 | 77.77 | 0.192 | 67.87 | 84.71 | 66.61 | 0.316 | 57.64 | 73.79 | | | FD-RL | 78.38 | 0.193 | 68.21 | 86.22 | 76.33 | 0.214 | 67.16 | 83.22 | 67.04 | 0.298 | 58.82 | 72.08 | | | Logics-Parsing-v2 | 76.35 | 0.213 | 67.67 | 82.67 | 73.85 | 0.248 | 67.33 | 79.02 | 67.64 | 0.304 | 61.65 | 71.64 | | | dots.ocr | 72.01 | 0.248 | 61.37 | 79.51 | 65.95 | 0.307 | 56.67 | 71.86 | 55.68 | 0.403 | 47.70 | 59.63 | | | Qianfan-OCR | 57.22 | 0.370 | 49.79 | 58.83 | 50.85 | 0.438 | 44.41 | 51.96 | 45.06 | 0.494 | 39.08 | 45.53 | | General VLMs | Qwen3-VL-8B | 72.44 | 0.261 | 65.10 | 78.35 | 72.03 | 0.266 | 64.88 | 77.82 | 62.73 | 0.342 | 55.55 | 66.81 | | | Kimi K2.6 | 72.32 | 0.303 | 66.93 | 80.30 | 69.95 | 0.322 | 64.69 | 77.31 | 68.02 | 0.335 | 62.44 | 75.14 | | | Gemini-3.1-Pro | 70.04 | 0.306 | 65.63 | 75.08 | 69.28 | 0.322 | 65.81 | 74.24 | 71.98 | 0.300 | 68.62 | 77.26 | | | Qwen3.5-397B-A17B | 69.12 | 0.233 | 65.26 | 65.40 | 68.34 | 0.244 | 63.91 | 65.53 | 62.70 | 0.287 | 60.70 | 56.12 | * * * ### [ICDAR2026 Sci-ImageMiner](https://sites.google.com/view/sci-imageminer/) | # | Team | RMS | TEDS | Weighted | | --- | --- | --- | --- | --- | | 1 | TeleOCR | 17.23 | 66.39 | 41.81 | | 2 | VLMinators | 17.29 | 64.31 | 40.80 | | 3 | Ricoh\_SRCB | 16.23 | 61.12 | 38.67 | | 4 | Vassilis Sioros | 14.94 | 55.20 | 35.07 | | 5 | DocMiner | 12.67 | 53.72 | 33.19 | | 6 | Qwen3 VL 8B | 14.08 | 57.86 | 35.97 | * * * ## πŸš€ Installation ``` pip install transformers torch pillow ``` ## Quick Start ``` import html import itertools import json import re from dataclasses import dataclass from PIL import Image import torch from transformers import AutoProcessor, AutoModel @dataclass class ContentBlock: type: str bbox: list[float] angle: int | None = None content: str | None = None @dataclass class TableCell: text: str start_row_offset_idx: int end_row_offset_idx: int start_col_offset_idx: int end_col_offset_idx: int row_span: int = 1 col_span: int = 1 OTSL_NL = "" OTSL_FCEL = "" OTSL_ECEL = "" OTSL_LCEL = "" OTSL_UCEL = "" OTSL_XCEL = "" OTSL_TOKENS = [OTSL_NL, OTSL_FCEL, OTSL_ECEL, OTSL_LCEL, OTSL_UCEL, OTSL_XCEL] def _otsl_extract_tokens_and_text(text: str): pattern = "(" + "|".join(map(re.escape, OTSL_TOKENS)) + ")" tokens = re.findall(pattern, text) parts = [part for part in re.split(pattern, text) if part.strip()] return tokens, parts def _count_right(rows, row_idx, col_idx, tokens): span = 0 while col_idx < len(rows[row_idx]) and rows[row_idx][col_idx] in tokens: span += 1 col_idx += 1 return span def _count_down(rows, row_idx, col_idx, tokens): span = 0 while row_idx < len(rows) and col_idx < len(rows[row_idx]) and rows[row_idx][col_idx] in tokens: span += 1 row_idx += 1 return span def _otsl_parse_texts(parts, tokens): rows = [list(row) for is_nl, row in itertools.groupby(tokens, lambda token: token == OTSL_NL) if not is_nl] if not rows: return [], [] max_cols = max(len(row) for row in rows) for row in rows: row.extend([OTSL_ECEL] * (max_cols - len(row))) cells = [] row_idx = 0 col_idx = 0 for idx, part in enumerate(parts): if part in (OTSL_FCEL, OTSL_ECEL): cell_text = "" right_offset = 1 if part != OTSL_ECEL and idx + 1 < len(parts) and parts[idx + 1] not in OTSL_TOKENS: cell_text = parts[idx + 1].strip() right_offset = 2 next_right = parts[idx + right_offset] if idx + right_offset < len(parts) else "" next_bottom = rows[row_idx + 1][col_idx] if row_idx + 1 < len(rows) and col_idx < len(rows[row_idx + 1]) else "" col_span = 1 + (_count_right(rows, row_idx, col_idx + 1, {OTSL_LCEL, OTSL_XCEL}) if next_right in {OTSL_LCEL, OTSL_XCEL} else 0) row_span = 1 + (_count_down(rows, row_idx + 1, col_idx, {OTSL_UCEL, OTSL_XCEL}) if next_bottom in {OTSL_UCEL, OTSL_XCEL} else 0) cells.append(TableCell( text=cell_text, row_span=row_span, col_span=col_span, start_row_offset_idx=row_idx, end_row_offset_idx=row_idx + row_span, start_col_offset_idx=col_idx, end_col_offset_idx=col_idx + col_span, )) if part in (OTSL_FCEL, OTSL_ECEL, OTSL_LCEL, OTSL_UCEL, OTSL_XCEL): col_idx += 1 elif part == OTSL_NL: row_idx += 1 col_idx = 0 return cells, rows def convert_otsl_to_html(otsl_content: str) -> str: if otsl_content.startswith(""): return otsl_content tokens, parts = _otsl_extract_tokens_and_text(otsl_content) cells, rows = _otsl_parse_texts(parts, tokens) if not cells or not rows: return "" grid = [[None for _ in range(len(rows[0]))] for _ in range(len(rows))] for cell in cells: for row_idx in range(cell.start_row_offset_idx, min(cell.end_row_offset_idx, len(rows))): for col_idx in range(cell.start_col_offset_idx, min(cell.end_col_offset_idx, len(rows[0]))): grid[row_idx][col_idx] = cell html_rows = [] for row_idx, row in enumerate(grid): html_rows.append("") for col_idx, cell in enumerate(row): if cell is None or cell.start_row_offset_idx != row_idx or cell.start_col_offset_idx != col_idx: continue attrs = "" if cell.row_span > 1: attrs += f' rowspan="{cell.row_span}"' if cell.col_span > 1: attrs += f' colspan="{cell.col_span}"' html_rows.append(f"{html.escape(cell.text.strip())}") html_rows.append("") return "" + "".join(html_rows) + "
" def post_process(blocks: list[ContentBlock]) -> list[ContentBlock]: for block in blocks: if block.type == "table" and block.content: block.content = convert_otsl_to_html(block.content) elif block.type == "equation" and block.content: content = block.content.strip() content = content.removeprefix("\\[").removesuffix("\\]").strip() if not (content.startswith("$") and content.endswith("$")): content = f"$${content}$$" block.content = content return [block for block in blocks if block.type != "equation_block"] def infer(image: Image.Image, prompt: str) -> str: messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": [ {"type": "image"}, {"type": "text", "text": prompt}, ]}, ] chat_prompt = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) inputs = processor( text=[chat_prompt], images=[image.convert("RGB")], padding=True, return_tensors="pt", ).to(device=model.device, dtype=model.dtype) output_ids = model.generate( **inputs, use_cache=True, max_new_tokens=4096, do_sample=False, ) output_ids = output_ids.cpu().tolist()[0][len(inputs.input_ids[0]):] return processor.batch_decode( [output_ids], skip_special_tokens=True, clean_up_tokenization_spaces=False, )[0].strip() processor = AutoProcessor.from_pretrained("StarDoc-AI/TeleOCR", trust_remote_code=True, use_fast=True) model = AutoModel.from_pretrained( "StarDoc-AI/TeleOCR", trust_remote_code=True, torch_dtype=torch.bfloat16, ).cuda().eval() image=Image.open("./assets/text.png").convert("RGB") raw_text = infer(image, "Please output the text content from the image.") print(raw_text.strip()) image=Image.open("./assets/table.png").convert("RGB") raw_otsl = infer(image, "This is the image of a table. Please output the table in OTSL format.") print(convert_otsl_to_html(raw_otsl)) image=Image.open("./assets/formula.png").convert("RGB") raw_formula = infer(image, "Please write out the expression of the formula in the image using LaTeX format.") formula_block = ContentBlock("equation", [0.0, 0.0, 1.0, 1.0], content=raw_formula) formula = post_process([formula_block])[0].content print(formula) #code image=Image.open("./assets/code.png").convert("RGB") raw_code = infer(image,"The image contains a code snippet, please output the parsing result.") print(raw_code.strip()) image=Image.open("./assets/layout.jpg").convert("RGB") image = image.resize((1036, 1036), Image.Resampling.BICUBIC) raw_layout = infer(image, "Analyze the image layout.") print(raw_layout.strip()) layout_image = Image.open("./assets/layout_distorted.jpg").convert("RGB") layout_image = layout_image.resize((1036, 1036), Image.Resampling.BICUBIC) raw_layout = infer(layout_image, "\nMulti-point Layout Segmentation Analysis.") print(raw_layout.strip()) #scientific figure image=Image.open("./assets/scientific_figure.png").convert("RGB") raw_scientific_figure = infer(image, "This is a scientific figure. Please extract the table implied by this figure.") print(convert_otsl_to_html(raw_scientific_figure)) ``` If you would like to perform complete document parsing, please refer to our GitHub repository: [https://github.com/caipeng328/NaviDC-OCR](https://github.com/caipeng328/NaviDC-OCR). ## Citation ``` @article{teleocr, title={TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents}, author={Cai, Peng and Zou, Zhaofan and Liu, Shifa and Wang, Yikun and Tang, Jiawei and Yang, Kaicheng and Tong, Meng and He, Zhongjiang and Sun, Hao}, journal={arXiv preprint arXiv:2608.12898}, year={2026} } ``` ## Community Contributions Thanks to Nandraj for the GGUF conversion and llama.cpp support! [πŸ”— NaviDC-OCR-GGUF](https://huggingface.co/nandraj/NaviDC-OCR-GGUF) ## Acknowledgements TeleOCR is built upon - MinerU - Qwen2.5-VL - Qwen3 - Transformers - PyTorch - FlashAttention We sincerely thank these excellent open-source projects. * * * ## Contact If you have any questions, feel free to open an issue or contact us. ## Want more deterministic results? [Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)