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Amazon Rekognition vs Tesseract.js: What are the differences?
- Technology and Purpose: Amazon Rekognition uses deep learning technology for image analysis, providing facial recognition, object detection, and image recognition capabilities. On the other hand, Tesseract.js focuses on text extraction from images using OCR techniques, making it ideal for tasks like scanning documents or extracting text from photos.
- Deployment and scalability: Amazon Rekognition is a managed service that offers scalable and cloud-based image analysis capabilities, making it suitable for enterprise-level applications with large amounts of data. In contrast, Tesseract.js runs entirely on the client-side, which limits its scalability and performance compared to Amazon Rekognition's cloud-based infrastructure.
- Supported languages: Amazon Rekognition supports multiple programming languages through its SDKs, making it accessible for developers using different programming environments. Tesseract.js, being a JavaScript library, primarily targets web developers and can be integrated into web applications easily using JavaScript.
- Accuracy and Performance: Amazon Rekognition is known for its high accuracy in image analysis tasks due to its sophisticated deep learning models and continuous training by Amazon. Tesseract.js, while capable of decent OCR performance, may not always match the accuracy levels of specialized image recognition services like Amazon Rekognition.
- Cost and Pricing model: Amazon Rekognition follows a pay-as-you-go pricing model based on the number of image analyses performed, making it cost-effective for applications with varying demands. Tesseract.js, being open-source, is free to use, but it requires additional resources for deployment and maintenance, making it potentially more expensive in the long run for certain use cases.
In Summary, Amazon Rekognition and Tesseract.js differ in their technology focus, deployment models, scalability, language support, accuracy, and pricing strategies.
Decisions about Amazon Rekognition and Tesseract.js
Vladyslav Holubiev
Sr. Directory of Technology at Shelf · | 1 upvote · 46.7K views
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.
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- Integrate easily with AWS4
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- Graph Recognization2
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Cons of Amazon Rekognition
Cons of Tesseract.js
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- AWS1
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What is Amazon Rekognition?
Amazon Rekognition is a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API enables you to quickly add sophisticated deep learning-based visual search and image classification to your applications.
What is Tesseract.js?
This library supports over 60 languages, automatic text orientation and script detection, a simple interface for reading paragraph, word, and character bounding boxes. Tesseract.js can run either in a browser and on a server with NodeJS.
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What companies use Amazon Rekognition?
What companies use Tesseract.js?
What companies use Amazon Rekognition?
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What tools integrate with Amazon Rekognition?
What tools integrate with Tesseract.js?
What tools integrate with Amazon Rekognition?
What tools integrate with Tesseract.js?
No integrations found
What are some alternatives to Amazon Rekognition and Tesseract.js?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
OpenCV
OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.
Google Cloud Vision API
Google Cloud Vision API enables developers to understand the content of an image by encapsulating powerful machine learning models in an easy to use REST API.
Tesseract OCR
Tesseract was originally developed at Hewlett-Packard Laboratories Bristol and at Hewlett-Packard Co, Greeley Colorado between 1985 and 1994, with some more changes made in 1996 to port to Windows, and some C++izing in 1998. In 2005 Tesseract was open sourced by HP. Since 2006 it is developed by Google.
libpng
It is the official Portable Network Graphics (PNG) reference library. It is a platform-independent library that contains C functions for handling PNG images. It supports almost all of PNG's features, is extensible, and has been widely used and tested.