Google Vision API v/s Microsoft Vision API
Both of them are promising and really make our life easy with a new way to think.
Feature Google Vision Microsoft Vision Objects Detection Yes Yes OCR Yes Yes Facial Detection Yes Yes Landmark Detection Yes No Logo/Brand/Person Detection Yes Yes Inappropriate content detection Yes Yes
For object detection both the API gave pretty decent results, I tried a fruit salad image and another one from a beach.
Here are the results I got
Google Microsoft Label Annotations:
vacation — Score:0.95443827
swimming pool — Score:0.67789888
People detected: 1 Image Details:(
Clearly Microsoft Vision API object recognition is more detailed Over Google Vision API.
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OCR of Google vision API is much much better than Microsoft Vision API. Microsoft worked little better in B/W image scans but Google Vision was amazing. So if you are looking more for OCR go for Google vision API. So far there is a tie between both 1/1.
Google Microsoft 10 People detected full of Joy. 15 faces detected, out which 14 were Happy and 1 neutral
Both of them were pretty accurate in Facial detection, Microsoft was little better with more accuracy and detection.
Microsoft also gives you a lot of face matching , grouping similar faces and face search features with its Face API. This a pretty cool in a lot of use cases. Google
Logo/Brand — Google was able to recognize most of the brands but Microsoft was not heavy on this one.
People — Microsoft has a celebrity detection feature which is really cool, Google visio doesn’t support this so far. It was able to detect most celebrities like the famous girl from Quantico.
Landmark — Google vision API is really good in this as compared to Microsoft vision.
Inappropriate Content Detection
Kissing images — Both of the API’s were ok
Adult/Nude content — Both the API’s were able to identify and label images accurately. Google was little better than Microsoft in identifying images like operations different from adult images.
This is a pretty usecase powerful for all product which rely on user generated content.
Originally published at Next Gen Technology Insider.