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A practical, ultra-lightweight open-source toolkit based on PaddlePaddle for multilingual Optical Character Recognition (OCR) and document parsing. Supports recognition of over 80 languages, includes data annotation and synthesis tools, and enables flexible training and deployment on servers, mobile, embedded, and IoT devices.
PaddleOCR is a comprehensive and efficient toolkit for Optical Character Recognition (OCR) and document parsing, built upon the PaddlePaddle deep learning framework. It offers robust multilingual support and is optimized for lightweight deployment.
Traditional OCR solutions can be expensive, lack broad language support, or are difficult to deploy across diverse hardware. PaddleOCR addresses these challenges by providing a free, open-source, highly multilingual, and versatile toolkit that is deployable on a wide range of devices.
Supports recognition of over 80 languages, making it suitable for global applications.
Designed for efficiency, allowing deployment on resource-constrained devices.
Provides tools to help users create and expand their own training datasets.
Offers capabilities for training custom models and deploying them across various platforms including server, mobile, embedded, and IoT devices.
PaddleOCR's versatility makes it applicable across various domains and scenarios where text extraction from images or scanned documents is required, especially in environments involving multiple languages.
Automate the conversion of scanned paper documents, PDFs, or images containing text in different languages into editable and searchable digital formats.
Significantly reduces manual data entry time and effort, improving efficiency in handling international documents.
Extract text data from images like photos of signs, product labels, or infographics for information retrieval, analysis, or translation.
Unlocks data embedded in visual content, enabling new forms of analysis and information processing.
Deploy the lightweight models on edge devices (e.g., surveillance cameras, handheld scanners, mobile apps) to perform OCR locally without needing a constant network connection.
Provides real-time text recognition in remote or network-constrained environments, enhancing application responsiveness and privacy.
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