To interface with the Amazon Rekognition API, we need to use the boto3 package: the AWS SDK. You will need additional information from our companion site for instructions on obtaining your AWS Rekognition keys. We’ll wrap up this tutorial with a discussion of our results. Send the API request to AWS Rekognition for OCR.Next, we’ll implement our Python configuration file (which will store our access key, secret key, and AWS region) and then create our driver script used to: Finally, we’ll use the boto3 package to interface with Amazon Rekognition OCR API. We’ll then show you how to install boto3, the Amazon Web Services (AWS) software development kit (SDK) for Python. These keys will include a public access key and a secret key, similar to SSH, SFTP, etc. The first part of this tutorial will focus on obtaining your AWS Rekognition Keys. If you need help configuring your development environment for OpenCV, we highly recommend that you read our pip install OpenCV guide - it will have you up and running in a matter of minutes. Luckily, OpenCV is pip-installable: $ pip install boto3 To follow this guide, you need to have the OpenCV library installed on your system. In upcoming tutorials, we will cover Microsoft Azure Cognitive Services and Google Cloud Vision API. In this tutorial, you’ll learn how to use the Amazon Rekognition API to OCR images. These companies have an incredible amount of image data - and when they train their novel, state-of-the-art OCR models on their data, the result is an incredibly robust and accurate OCR model. Then consider the amount of data Amazon generates daily from simply printing shipping labels. So first, consider the amount of data that Google and Microsoft have from running their respective search engines. Looking at the previous list, you may wonder why on earth would we cover these APIs at all - what is the benefit?Īs you’ll see, the primary benefit here is accuracy. They cost money (but typically offer a free trial or are free up to a number of monthly API requests).Due to the latency and amount of time it will take to OCR each image, it’s doubtful that these OCR APIs will be able to run in real-time.The API will need to chew on the image and OCR it, and then finally return the results to the client OCR results will take longer because the image needs to be packaged into an API request and uploaded to the OCR API.There will be latency introduced by the network connection.Additionally, if you are working with edge devices, then you may not want to spend the power draw on a network connection.An internet connection is required to OCR images - that’s less of an issue for most laptops/desktops, but if you’re working on the edge, an internet connection may not be possible.While these models do tend to be more accurate than Tesseract, there are some downsides, including: To keep these models and associated datasets proprietary, the companies do not distribute the models themselves and instead put them behind a REST API. Typically, these OCR engines live in the cloud.
However, other optical character recognition (OCR) engines are available, some of which are far more accurate than Tesseract and capable of accurately OCR’ing text, even in complex, unconstrained conditions. So far, we’ve primarily focused on using the Tesseract OCR engine. Text Detection and OCR with Amazon Rekognition API Looking for the source code to this post? Jump Right To The Downloads Section
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