ddddocr
带带弟弟OCR
Decision gist · record as of 2026-08-14
Yes, if you need offline captcha recognition and accept the trade-off that accuracy depends on model fit to your specific captcha style. The permissive MIT license, active maintenance, low install friction, and no known vulnerabilities make it a reasonable choice for development and production. Start with the default model; if accuracy is poor, try the beta model or train a custom one.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; GPU acceleration needs CUDA and onnxruntime-gpu separately installed.
- Low friction install with pure-Python wheel and five runtime dependencies (numpy, onnxruntime, Pillow, opencv-python variants).
- Active maintenance with recent commits and 14606 GitHub stars.
License · maintenance · safety
permissive license (permissive) — MIT license permits free use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
last release 2026-03-11 (156 days) · last repo commit 2026-03-10 · 14,606 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 416,565 downloads/mo, #6,819 on PyPI
Alternatives
Verify before relying
pip install ddddocr
import ddddocr
ocr = ddddocr.DdddOcr()
with open('captcha.jpg', 'rb') as f:
result = ocr.classification(f.read())
print(result)- Actual recognition accuracy rates across different captcha types and complexity levels
- Memory footprint and latency benchmarks for batch processing
- Whether custom model training via dddd_trainer is documented and accessible
What it is and what it does
ddddocr is an offline, local captcha recognition library that uses pre-trained neural network models to identify text and objects in images. It handles common alphanumeric captchas, Chinese characters, slider puzzles, and special characters without requiring external API calls. The library wraps ONNX Runtime models and depends on numpy, Pillow, and OpenCV for image processing.
You initialize it once, then call `classification()` on image bytes to get recognized text. It supports multiple modes—standard OCR, object detection, slider matching—and allows GPU acceleration if you have CUDA available. The package also provides probability distributions per character and lets you constrain recognition to specific character sets, useful when you know the captcha format in advance.
Use it for
- Automate login flows that require solving simple alphanumeric captchas in web scraping or testing
- Batch-process captcha images offline without hitting rate limits or costs of external OCR services
- Detect and locate objects or text regions in images for accessibility or content moderation workflows
- Recognize Chinese character captchas or mixed-language verification codes in regional applications
- Match slider puzzle gaps by comparing edge patterns between template and target images
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need offline captcha recognition and accept the trade-off that accuracy depends on model fit to your specific captcha style.
The permissive MIT license, active maintenance, low install friction, and no known vulnerabilities make it a reasonable choice for development and production. Start with the default model; if accuracy is poor, try the beta model or train a custom one.
Install
ddddocr on PyPI
Before you install
Low friction install with pure-Python wheel and five runtime dependencies (numpy, onnxruntime, Pillow, opencv-python variants). Active maintenance with recent commits and 14606 GitHub stars.
Requires Python 3.10 or later; GPU acceleration needs CUDA and onnxruntime-gpu separately installed.
License in practice
MIT license permits free use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
Quickstart
pip install ddddocr
import ddddocr
ocr = ddddocr.DdddOcr()
with open('captcha.jpg', 'rb') as f:
result = ocr.classification(f.read())
print(result)Verify before relying
- Actual recognition accuracy rates across different captcha types and complexity levels
- Memory footprint and latency benchmarks for batch processing
- Whether custom model training via dddd_trainer is documented and accessible
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesnumpyonnxruntimePillowopencv-pythonopencv-python-headless |
| Maintenance | Actively maintained 156 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 416,565 / month, #6,819 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: ddddocr-1.6.1-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “ocr verification code”
- ddddocrRecognizes text and detects objects in captcha images using offline…
- kreuzbergExtracts text, tables, images, and metadata from 91+ file formats…
- extend-aiOfficial Python SDK for the Extend document processing API, enabling…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also qrdet · qudida · perceptron · rapidocr-onnxruntime · surya-ocr · anticaptchaofficial · qreader · paddlex · easyocr · captcha