When analyzing regular pics vs. Pics with Halograms embedded inside

Computer Vision API doesn't recognize very clear and obvious text surrounded by @ character

I tried your demo at https://azure.microsoft.com/en-gb/services/cognitive-services/computer-vision/ with my image. Then I created my test app and used similar code with eastAsia server. Your demo is giving perfect results but my app is generating different text. I am wondering why this is happening. Actually I am using the same bot but not getting same results. Is there anything that I am missing?
Please give me feedback on the same.

Function to search in whole Blob with Images to recognize duplicated images.

Can the API detect humans even from a back view. It might not be very accurate just like humans but after you have lived with someone else for a long time there is a high chance that you can guess who they are even from a back view. Therefore was wondering if the computer detected multiple images of people even from back view whether it would be able to guess.

The results using the website (https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/#text) are very good. But when I use the API, it's very poor. How would you explain the difference in the results? I used the exact code from this website and even used images from the website to test (Like the green closed sign).
i use v2.0 and both westcentralus and westeurope
Does the website do some preprocessing? like rotating the text?

Trying to find text on an image with hollow text doesn't return the right text results from the image and sometimes returns none at all.

The guide for recognizeText is not available for php. So I would like to add one. How should I add?

Feature like to compare the image with master image (Good Image) and tell a difference between two images.

Could we have the API File size limit increased? currently we work with a lot of imagery with high resolution images that average ~8MB.

Can the Size limit be increased

It would be great if we can specify a parameter for OCR to just read printed or handwritten text. In the case of scanned documents it would improve the accuracy and reduce the API's workload.
It would also make it easier to visualize the detection and read/check the accuracy of large batches of tests

I've successfully been able to use vision to extract handwritten text out of fixed forms by knowing the coordinates of each form field. However many of my forms have checkboxes and/or radio buttons that the users will be filling in with pen. It doesn't seem that vision has a way to detect this type of content.

You have two images like tax forms. Compare the two images. Return a confidence they are the same. Return a set of cords for the image where the primary differences occur. PDF as well as jpg and bmp support would be nice.

Firebase started offering offline on-device image recognition.

https://firebase.google.com/products/ml-kit/

Are there any plans to allow use to do something like OCR offline like Google does?

In the OCR API from Computer Vision API - v2.0, for every word I get the text and the bounding rectangle. Would it be posible to also get the font type and optionally font size/other parameters?
Usage scenario: overlay new text over the old one, for example live translate.

My applications allows end-users to upload images that are used on the site. Users do not understand how to upload images with the correct aspect ratio, size, pixel ratio, etc. I need tools to allow me to process these images and correct these issues. I am currently using Cloudinary but would consider this Azure service if you can add these features.

Help me Identify dog or cat by there nose print. it is can be use for other animal not only for Human face or fingerprint.