Computer vision continues to be one of the hottest topics in the tech industry. With image/video recognition being considered the core of it, it’s been able to transform businesses using innovative techniques. Image recognition is the technology that can identify places, people, buildings, objects, etc. from digital images/videos. The process of detecting and analysing objects from images allow businesses to enable the automation of a specific task. After assessing a business process and finding a need for image recognition, it can be quite overwhelming trying to choose the right service for your project.
As expected, Google and Amazon have stepped into the ring and have each come up with their own impressive line of service offering. A short analysis and comparison of each will be conducted along with benefits of building a model from square one to not restrict oneself to a proprietary non-portable solution.
Google’s Cloud Vision AI is pre-configured to tackle the most common image recognition tasks, like detect emotion, understand text and more. Google offers these services through two products, AutoML Vision and Vision API. AutoML Vision automates the training of custom machine learning models, whilst Vision API offers powerful pre-trained machine learning models through REST and RPC APIs. OCR (Optical Character Recognition) is highly known as one of Vision API’s best feature. The API can detect printed and handwritten text from an image, PDF, or TIFF file.
Launched in 2016, Amazon Rekognition handles the common tasks much like Google, with some added features beneficial for video processing. One of Amazon’s best feature is capture movement which allows one to track an object’s movement through a frame. Much like Vision AI, both services offer a free number of x images before a quote is applied.
For an extensive library of pre-configured recognition models and quality handwriting recognition, Google Vision API could prove to be more useful. Whilst Amazon Rekognition would be a better fit for celebrity recognition or movement capture. Overall, they perform the same basic image recognition features. Rekognition is more user-friendly for those with no technology background. Google is known to be more expensive than Amazon, however the pricing does come down to the feature applied to the image and the number of hours required by training.
Ensure when choosing an image recognition API, the offerings are analysed over the following criteria: visual analysis features, types of visual data and analysis mode, pricing, API usage and technical support. Popular APIs tend to be a reliable option, however with unlimited access to open-source frameworks and libraries, building your own is a feasible option especially for the projects that require custom solutions with very specific needs. Therefore, developing your own neural network and models from scratch gives you more flexibility.
With the numerous factors to consider, it is important to ensure the proposed solution meets the specific needs of the initial task. No matter how its approached, image recognition is made stronger by access to more pictures and real-time big data. For a better understanding, please contact Reply for a professional consultation or check us out on