Hi, This week you'll learn about Text Detection and OCR with Microsoft Cognitive Services. Last week's tutorial dealt with using Amazon Web Services (AWS) Rekognition API for our deep learning project. Since AWS isn't the only state-of-the-art cloud service in the market, we decided to familiarize you with another great cloud-based service: Microsoft Cognitive Services (MCS). Boasting an array of cognitive services (e.g., speech, sentiment analysis, face detection, risk assessment, and fraud detection), MCS is a leader in cognitive computing, second only to AWS. In terms of efficiency, accuracy, and overall performance, MCS is at par with its competitors. However, the only area that it is beat is its implementation's higher complexity. Let's see it in action in today's tutorial, Text Detection and OCR with Microsoft Cognitive Services. The big picture: MCS (part of Microsoft Azure) hosts many cloud-based artificial intelligence (AI) services to help build cognitive intelligence into applications. While it is a tad bit more complicated to implement than AWS, there can be several situations where MCS can be used. Especially when dealing with low-quality input, MCS works well in these cases. How it works: Microsoft Cognitive Services are available to you as user interfaces, REST APIs, and client library SDKs. The user can choose any of the available options based on where they are comfortable. The cloud-based services have cutting-edge layered protection to keep your projects secure and provide multilingual support. Our thoughts: Microsoft Cognitive Services as a whole offers many choices in the services they provide. As with other cloud services, anyone without data science or AI expertise can use MCS. Yes, but: It is more complex in usage when compared to AWS. However, performance-wise, this leaves a lot of credibility for its use in real-life scenarios, as both are similar. Hence people would opt for the easier-to-use AWS. Stay smart: The complexity in implementation doesn't take anything away from MCS regarding performance. People who are comfortable with Azure, in general, should definitely try and master MCS further. As the implementation is the only noticeable barrier to cross while using Azure, there's no reason why we shouldn't use MCS if that's no longer a problem. Click here to read the full tutorial PyImageSearch University This lesson is part of PyImageSearch University, our flagship program to help you master computer vision, deep learning, and OpenCV. PyImageSearch University is updated each week with new lessons. Don't spend your time trying to piece together code when you could get pristine code right now. Want the full, working code of every blog post plus a Colab Notebook and video walkthrough of the code? I want the code Having problems with your local development environment or IDE? Fortunately, our pre-configured Colab Notebooks allow you to run code the moment you join PyImageSearch University. But, of course, you don't want to be a sys-admin, so don't waste time messing with your development environment. Want to Master Computer Vision and Deep Learning? Do you think mastering computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Or has to involve complex mathematics and equations? Or requires a degree in computer science? That's not the case. All you need to master computer vision and deep learning is for someone to explain things to you in simple, intuitive terms. And that's exactly what we do. Our mission is to change education and how complex Artificial Intelligence topics are taught. Inside PyImageSearch University, you'll find: - Code that works for almost any OpenCV or TensorFlow CV problem
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