The model was removed from huggingface, so I re-uploaded it here from modelscope repo (the MIT license allows this).

dots.ocr-1.5: Recognize Any Human Scripts and Symbols

🖥️ Live Demo | 💬 WeChat | 📕 rednote

Introduction

Evaluation of Vision-Language Parsing

Note:

• We use the ISVGEN metric from UniSVG to evaluate the parsing result. For benchmarks that do not natively support image parsing, we use the original images as input, and calculate the ISVGEN score between the rendered output and the original image.
• OCRVerse results are derived from various code formats (e.g., SVG, Python), whereas results for Gemini 3 Pro and dots.ocr-1.5 are based specifically on SVG code.
• Due to the capacity constraints of a 3B-parameter VLM, dots.ocr-1.5 may not excel in all tasks yet like svg. To complement this, we are simultaneously releasing dots.ocr-1.5-svg. We plan to further address these limitations in future updates.

Quick Start

1. Installation

Install dots.ocr-1.5

Download Model Weights

💡Note: Please use a directory name without periods (e.g., DotsOCR_1_5 instead of dots.ocr-1.5) for the model save path. This is a temporary workaround pending our integration with Transformers.

2. Deployment

vLLM inference

4. Demo

Examples for image parsing

Limitation & Future Work

• Complex Document Elements:
  • Table&Formula: The extraction of complex tables and mathematical formulas persists as a difficult task given the model's compact architecture.
  • Picture: We have adopted an SVG code representation for parsing structured graphics; however, the performance has yet to achieve the desired level of robustness.
• Parsing Failures: While we have reduced the rate of parsing failures compared to the previous version, these issues may still occur occasionally. We remain committed to further resolving these edge cases in future updates.