What is the best way of recognizing handwritten symbols and texts?

 

I am new to the image processing subject. I'm using opencv library for image processing with python. I need to extract symbols and texts related to those symbols for further work. I saw some of developers have done handwritten text recognitions with Neural network, KNN and other techniques.

My question is what is the best way to extract these symbols and handwritten texts related to them?

Example diagram: 在这里输入图像描述

Details I need to extract:

  • No of Circles in the diagram.
  • What are the texts inside them.
  • What are the words within square brackets.
  • Are they connected with arrows or not.

    • Of course, there is a method called SWT - Stokes Width Transform.

    Please see this paper, if you search it by its name, you can find the codes that some students have written during their school project.

    By using this method, text recognitions can be applied. But it is not a days job.

    Site: Detecting Text in Natural Scenes with
    Stroke Width Transform

    Hope that it helps.

    For handwritten text recognition, try using TensorFlow. Their website has a simple example for digit recognition (with training data). You can use it to implement your own application for recognizing handwritten alphabets as well. (You'll need to get training data for this though; I used a training data set provided by NIST.)

    If you are using OpenCV with python, Hough transform can detect circles in images. You might miss some hand drawn circles, but there are ways to detect ovals and other closed shapes.

    For handwritten character recognition, there are lots of libraries available.

    Since you are now to this area, I strongly recommend LearnOpenCV and and PyImageSearch to help you familiarize with the algorithms that are available for this kind of tasks.

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