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.

How to send a local image instead of URL to Microsoft Computer Vision API using JAVA

MS Handwriting recognition is best-in-class in reading poor handwriting, but seems algorithm chooses not to recognize individual symbols or characters by themselves. All text is not written in full words.

The following examples never process the same symbol correctly on successive lines. Symbols, or even "0" or "o" are ignored when left by themselves.

I have trained a project at customvision.ai/projects. How can I migrate / import this to my production Azure subscription?

To disambiguate similar names, include a DBpedia graph id in the response. This will help more with categorizing results and using the data in more meaningful ways.

To avoid homonymy problems, it would be very useful to add a unique identifier to recognized celebrities in the api response

postRequest =(String)("POST ") +"/vision/v1.0/analyze?https://upload.wikimedia.org/wikipedia/commons/1/12/Broadway_and_Times_Square_by_night.jpg HTTP/1.1\n"
+ "visualFeatures: Categories,Description,Color"
+ "language: en\n"
+ "Content-Type: application/json"
+ "Ocp-Apim-Subscription-Key: {mysub key}\n"
+ "Host: westus.api.cognitive.microsoft.com\n"
+ "Content-Length: 152\r\n\r\n";

As shown above, I'll get an error number of 401,which means Access denied due to missing subscription key , how do I organize the content of my post request ?

We have some standard forms that are in handwritten format but the pattern of the text is always the same (I think this can apply to a large number of scenarios). Allowing the handwritten recognizer to be configured with a expected pattern via a simple regex could improve a lot of scenarios.

While head tilt angle is useful for face detection, it would be incredible to see you support standard 25 (body, no fingers) and 65 (body w/fingers) bone placement for full-body images.

Being able to reliably pull out complex bounding areas would be a game changer, ideally if the classifier had an understanding of human proportion and dimension. eg. if someone is wearing a white shirt against a white background, or even if they have white buttons, you shouldn't carve a hole through the person.

Currently using Dlib facial detection which returns 68 facial landmarks vs Azure's 27. More is better; this is proving to be a political issue with the system architect.

1. I'm getting no regions back from about half of the of images I submit for recognition in the attached example file.

2. On full document scans, I often see the returned json resembling this:
dwoo ,4.Ed co c Ex—co cc äö30a-oF— Z U) r-x.l (DC—a- 0 00 o o O O

Can anyone suggest possible causes / solutions to either of these issues?

There is a need to use Computer Vision/OCR with report images. For that to work well it would be great if the API could make a guess/determination on the layout of the report. Today OCR returns boundingBox information which is helpful but if the API would identify field names from data values as well as header vs. detail fields that would be amazing!

Every shipping company and manufacturer chooses a different barcode that supports their needs, but they are not interchangeable, which is by design for automation, but we need to read all the barcodes and categorize them because we cant train our users to figure out which one to scan.

Please add support for Taiwan region. Thanks