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Google Cloud Vision API vs Tesseract OCR vs scanR: What are the differences?
Introduction: When comparing Google Cloud Vision API, Tesseract OCR, and scanR for Optical Character Recognition (OCR) tasks, it's essential to understand their key differences to make an informed decision.
Accuracy: Google Cloud Vision API utilizes powerful machine learning algorithms to achieve high accuracy in text recognition, making it suitable for complex documents and images. Tesseract OCR, an open-source OCR engine, provides decent accuracy but may require manual tuning for optimal results. scanR, on the other hand, offers reliable accuracy but may not be as robust as Google Cloud Vision API in handling diverse content formats.
Language Support: Google Cloud Vision API supports a wide range of languages, including less widely spoken languages, making it a versatile choice for multilingual OCR tasks. Tesseract OCR also offers excellent language support through language packs, while scanR may have limitations in recognizing less common languages and character sets.
Customization Options: Google Cloud Vision API provides customizable models for specific use cases, allowing users to train models with their own data for improved performance. Tesseract OCR offers customization through parameter tuning and training with new fonts, styles, or languages. scanR may not offer as extensive customization options as the other two platforms.
API Integrations: Google Cloud Vision API seamlessly integrates with other Google Cloud services, providing a scalable and robust OCR solution for cloud-based applications. Tesseract OCR can be integrated into various programming languages and platforms, offering flexibility in deployment. scanR may have limited API integration capabilities compared to the other two options.
In Summary, understanding the key differences in accuracy, language support, customization options, and API integrations among Google Cloud Vision API, Tesseract OCR, and scanR is crucial for selecting the most suitable OCR solution for your specific requirements.
AWS Rekognition has an OCR feature but can recognize only up to 50 words per image, which is a deal-breaker for us. (see my tweet).
Also, we discovered fantastic speed and quality improvements in the 4.x versions of Tesseract. Meanwhile, the quality of AWS Rekognition's OCR remains to be mediocre in comparison.
We run Tesseract serverlessly in AWS Lambda via aws-lambda-tesseract library that we made open-source.
Pros of Google Cloud Vision API
- Image Recognition9
- Built by Google7
Pros of scanR
Pros of Tesseract OCR
- Building training set is easy5
- Very lightweight library2
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Cons of Google Cloud Vision API
Cons of scanR
Cons of Tesseract OCR
- Works best with white background and black text1