handwriting | Chinese Handwriting Recognition with CNNs | Machine Learning library

I have huge data that consist of points(x,y) and need to present in a scatter plot.

To find overlap and remove invisible differences between the same point, I wrote bellow code :

I'm trying to use keras neural network of tensorflow to recognize the handwriting digit number. But idk why when i call predict(), it returns same results for all of input images.

Here is code:

I have been trying solve this problem for a while now for a university assignment. I'm required to build a DFA and an NFA for the above question. So far I have been able to solve the DFA, but can not find a solution for a proper NFA after multiple tries.

My attempts for the NFA are down below. I apologize for my messy handwriting but these were just rough works that I was drawing out on the go.

I have trained a KNN model to predict handwritten images in the MNIST dataset. I want to test it on my own handwriting now. I want to convert it into the MNIST format (values for 784 pixels in the image as an array). I tried converting the image into a 28*28 pixels and storing the pixel intensities in the code below:

I have a trained model (Which I trained myself), which is a .h5 format, It works fine by itself, but I need to convert it to .onnx format for deploying it inside Unity Engine, I searched how to convert .h5 models to .onnx format and stumbled upon keras2onnx library, and following some tutorials I got this:

I am trying to build a lucky draw system. There are 8 cards in each category and 8 people to assign those cards. I want to make a flipping animation on cards that all cards flip till 10 seconds then only one card appear on screen as jackpot. How can I achieve that with CSS and JavaScript?

My current code:-

CSS

I have this SVG handwriting logo animated by increasing the stroke-dashoffset.

Chrome browser is fine. But on Safari, the animation is going so wrong, the letters is covered on Safari. Is there some iOS specific bug stroke-dashoffset or something that I'm not aware of? codepen

I want to separate the handwriting from the background as perfectly as possible in images like the following:

It first looks like one can separate the pixels by color, but plotting the pixels by brightness and relative blue content does not give a clear separation:

Using the above separating lines to keep only the pixels in the upper left area (and set the other pixels to white) we get the following result:

The handwriting is not fully extracted, but the (printed) numbers start to appear. So an improvement of the separating lines does not seem to be possible. Any other possibilities to improve the result?

I am new on deep learning. To practicing I trained a simple Handwriting model with tensor-flow and mnist. After loading mnist I made model and trained that: