recognition | 传图识字小程序 | Computer Vision library

i'm writing a code using vosk ( for offline speech recognition), in my string.xml i wrote a string-array:

I am looking for an application or a tool which is able for example to extract data from a 2D contour plot like below :

I have seen https://dash-gallery.plotly.host/Portal/ tool or https://plotly.com/dash/ , https://automeris.io/ , but I have test them and this is difficult to extract data (here actually, the data are covariance matrices with ellipses, but I would like to extend it if possible to Markov chains).

If someone could know if there are more efficient tools, mostly from this kind of 2D plot. I am also opened to commercial applications. I am on MacOS 11.3.

If I am not on the right forum, please let me know it.

UPDATE 1:

I tried to apply the method in Matlab with the script below from this previous post :

UPDATE IN ANSWER BELOW

Is anyone else experiencing the newest couple versions of chrome causing issues with legacy Java applications? Just yesterday I needed to get the company's policy manager to allow downloading files from an internal unsecured server by adding our URLs to a whitelist - you can see the details of the process on the chromium blog here. That issue was present in v90 as well.

What I'm currently experiencing due to the v91 update is as follows: My boss was trying to use a page in one of our Java 6 legacy applications and he noticed that the page wouldn't return the data in any format - we checked and he was already v91. I was on v90 and the page worked fine. After updating Chrome to v91, I'm getting the same broken page as my boss.

I was thinking it might be something related to the CSS but I don't have time to poke at it and redeploy the legacy app every time to test the changes. Though, I have taken a peek at this chromium blog post for version 91. Though I don't see much relating to what may have caused the removal of all non-label fields and the formatting of the label fields are all wonky and out of place.

I'm going to look into investigating the struts tile that holds the code JSP code; if I find something I'll post it here for reference.

The first image below is what one row should look like with the header above it. As you see in the second picture, all there is the header with improper formatting and the grid is gone.

I'm creating a machine-learning program to recognize images that are shown on webcam. I've used Google Teachable Machine to generate the model and it works fine.

The matter I'm having issues with is printing the results of a prediction array, when an element of this array achieves a certain value (if it's equal to or more than 0.9 for an element, print a specific message).

Let's say when element prediction[0] >= 0.9 I want to execute print("Up") as it recognizes the image of an arrow facing up or if element prediction[1] >= 0.9 I'd do a print("Down") etc.

But when I try do that using the if statement I am presented with a

I want to design and train a neural network for the automatic recognition of the edges, in some microscopic images. I am using Keras for a start, I may consider PyTorch later.

The structure of the images is rather simple, with some dark areas, and some clear areas, relatively easy to distinguish, and the task is to select the pixels of the contour between dark and clear areas. The transition between dark and clear is gradual, so my result is not a single line of edge pixels, but rather a 10 or 15 pixels wide "ribbon" at the edge.

I have manually annotated 200-something images, so for each image I have another image, of the same size, where the pixels of the contours are black, and all the other pixels are white.

I have seen many tutorials on how to design, compile and fit a model (a neural network), and then how to test it, using the manually annotated data.

However, most of the tutorials work on problems of classification, where the number of neurons in the output layer is the number of categories.

My problem is not a problem of classification, and ideally my output should be an image of the same size of the input.

So, here is my question:

What is the best way to design the output layer? Is a layer with a number of neurons equal to the number of pixels the best idea? Or this is a waste, and there is a more efficient way?

Addendum

1. The images are "easy", but it is still difficult to find the contour pixels, so I believe that it is worth using the machine learning approach.
2. The transition between dark and clear is a little gradual, so my result is not a single line of pixels on the edge, but rather a band, a 10 or 15 wide ribbon of edge pixels. Since I am after a ribbon of pixels, my categories should be "edge" and "not-edge". If I use the categories "dark pixels" and "clear pixels", and then numerically find the pixels between the two areas I do not get the "ribbon" result, which I need.

I managed to write a code to decimate my video and take only 1 frame out of 10, in order to make my neural network more efficient in the future for character recognition. The new video exit_video is well decimated because it's way faster than the previous one.

1: When I print the fps of the new video, I have 30 again despite the decimation

2: Why is my new video heavier ? 50.000 ko and it was 42.000 ko for the firts one

Thanks for your help

so what I want to do is sending an string which is a path for my image .. I want to do that in routes file . because I receive in it the image path like that

I'm trying to import the NERDA library in order use it to engage in a Named-Entity Recognition task in Python. I initially tried importing the library in a jupyter notebook and got the following error:

I am using speechRecognition and I would like to replace some spoken words to emoji's.

This is my code: