If Sundar Pichai’s predictions come true, Artificial Intelligence will have more consequential impacts than fire, electricity, and the internet. This brings more clarity to the fact that Google is consistently trying to modify and update its Artificial Intelligence offerings. One among these is the Google Vision AI, a technology that helps users gain more insights on images through Machine Learning.
Google is keen on investing more into Vision AI, aiming to bring new updates to the technology while reducing its existing complexity. In this article, let us try to explore Google Vision AI in detail, and try to decipher if it is better than any open source software for optical character recognition. Let’s Start!
Application Programming Interfaces or APIs act as messengers that take our messages to the system and bring back the appropriate response for the final action. Machine learning, like other programs, is dependent upon several such APIs and one such API created by Google is the Vision AI.
With the help of a huge database of images prepared by Google and machine learning models with self-learning capabilities, the Google Vision AI allows users to segregate components of their images and understand them in a better way. The Google Vision AI aims at solving problems and complexities in existing technologies. It helps in optimizing processes like image recognition and image classification.
● Label Detection
The Google Vision AI offers many different services. The first service is called label detection. In label detection, the API identifies different elements of the image and then places them into categories. Depending on how accurately the process is done, a score of 0 or 1 is assigned.
● Landmark Detection
The Landmark detection feature is about identifying any natural or man-made landmark that appears in the image. Once a landmark is identified successfully, the output is generated. If the recognition cannot be done, there is no output, indicating how accurate the landmark detection feature of the Google API is.
● Facial Detection
To identify a particular face in an image consisting of multiple faces, the facial recognition algorithm is used. Again, the algorithm is quite accurate, but the emotional detection aspect is still being researched and worked upon.
Use Cases of Google Vision
● Aircraft Servicing
The Google Vision AI technology is being used extensively for aircraft maintenance systems. Using the API, the non-conformities in aircraft structures can be identified within seconds. This helps in locating a fault very accurately, and in resolving the issue fast. Otherwise, a traditionally serviced aircraft takes months to get the desired treatment and then be included back into the fleet.
● Security Systems
CCTV cameras are an integral part of the security management system in any commercial building. The use of Google API helps in recognizing any suspicious element within the routine video recordings done by these cameras. Since machine learning models are used in Google vision AI, they correlate every aspect of the recorded images with the database. Thus, any dangerous activity can be recognized automatically within seconds and then mechanisms like sirens or other warning alerts can be activated.
● E-Commerce Optimization
Artificial intelligence combined with Google Vision AI is proving like a game changer for e-commerce websites. The Google Vision AI API helps in segregating different product images as per their category automatically. Based on the user behavior studied and tracked through different artificial intelligence techniques, the right product images are placed before the right user successfully. For this to happen successfully, categorization of the image database is important and the Google Vision API does this quite accurately.
● Banking Sector
The banking sector is using the Google Vision AI for a different type of use case. Banks are flooded with application forms, with hundreds of details altogether. With the Google Vision AI API, these documents can be easily scanned and important characters can be studied to classify documents. This way, the analysis of Loan documents, KYC forms, account opening requests, etc. can be completed easily.
Optical Character Recognition is the technology in which a physical document is scanned and the text present in it is converted into a digital form. This machine-encoded text can then be uploaded onto the company’s portal and then be used for a hundred different processes. For the process to be meaningful, it has to be ensured that the output generated by the OCR software is accurate enough.
The problem with traditional OCR applications was that they could not handle the complexities in modern-day documents. Therefore, to accommodate different aspects like fonts, sizes, templates, etc., newer OCR solutions are being developed, and the one given by Google is just a good example of the same.
KlearStack offers the most advanced OCR software today. We provide OCR software solutions that are empowered by artificial intelligence capabilities. With artificial intelligence, the error-detection ability of the software improves very significantly. Moreover, data becomes more suitable for analytics and also for generation of actionable insights.