Digit-Recognition | Recognize handwritten digits using back-propagation | Machine Learning library

I'm completely new to machine learning and I wanted to start with a fairly easy project: the digit recognition using the mnist data set. I'm using keras and tensorflow and I started using code I found here.The network is built and trained correctly and I now want to make a simple prediction. For starters I simply used one of the pictures in the part of the data set meant for testing and I would like my output to be that number. (In this case the output is supposed to be 7.) Here's my code:

I am trying to write a simple character recolonization code using convolutional neural network in python on windows. I am following this tutorial. But somehow I am having following error message. I could not find the appropriate reason of this error. It would be helpful for me if anyone can breakdown the error with probable solution.

I am trying to locate rectangles in an image, and apply a classifier to recognize numbers inside each one, using a previously trained classifier:

I am trying to extend a basic classification model (https://machinelearningmastery.com/handwritten-digit-recognition-using-convolutional-neural-networks-python-keras/) to a simple object detection model for single objects.

The classification model simply classifies handwritten digits in images where the digit fills most of the image. To make a meaningful dataset for the object detection I use the MNIST dataset as base and transform it into a new dataset by the following steps

1. Increase the image canvas size from 28x28 to 100x100
2. Move the handwritten digit to a random position within the 100x100 image
3. Create a ground truth bounding box

Figure 1: Illustration of step 1 and 2.

Figure 2: Some produced ground truth bounding boxes.

The output vector from the model is inspired by the YOLO definition but for a single object:

I am new to Deep Learning and am using Keras to learn it. I followed instructions at this link to build a handwritten digit recognition classifier using MNIST dataset. It worked fine in terms of seeing comparable evaluation results. I used tensorflow as the backend of Keras.

Now I want to read an image file with a handwritten digit and predict its digit using the same model. I think the image needs to be transformed to be in 28x28 dimension with 255 depth first? I am not sure whether my understanding is correct to begin with. If so, how can I do this transformation in Python? If my understanding is incorrect, what kind of transformation is required?

Thank you in advance!

My goal is to classify MNIST handwritten digits using keras. I am trying to reproduce the results from this website.

When creating the model ("model = baseline_model()"), I get the error message AssertionError: Keyword argument not understood: kernel_initializer

Do you know how to solve this issue ? I am using keras 1.1.1 with theano back-end

I want to ask for suggestion on how to solve the problem of tesserocr did not recognize certain line from an image.

This is the image. source is from Simple Digit Recognition OCR in OpenCV-Python

The code

I'm completely new to keras and to get started, I'm trying to follow this tutorial. I use theano backend. Sadly, I already encounter a difficulty at this line:

I have a simple digit recognition project and have noticed that people generally use two approaches when doing so in Python. My goal is to input a PDF document and get the HANDWRITTEN digits in particular places of the page.

I saw that people either use opencv, as in this question, or scikitlearn, as is seen in this example. I am not familiar with either, and am wondering which one would be most simple to learn and implement, given my intended usage. Thanks.