Accurate binary image classification

Question

I'm trying to extract letters from a game board for a project. Currently, I can detect the game board, segment it into the individual squares and extract images of every square.

The input I'm getting is like this (these are individual letters):

At first, I was counting the number of black pixels per image and using that as a way of identifying the different letters, which worked somewhat well for controlled input images. The problem I have, though, is that I can't make this work for images that differ slightly from these.

I have around 5 samples of each letter to work with for training, which should be good enough.

Does anybody know what would be a good algorithm to use for this?

My ideas were (after normalizing the image):

• Counting the difference between an image and every letter image to see which one produces the least amount of error. This won't work for large datasets, though.
• Detecting corners and comparing relative locations.
• ???

Any help would be appreciated!

Answer 1

I had a similar problem few days back. But it was digit recognition. Not for alphabets.

And i implemented a simple OCR for this using kNearestNeighbour in OpenCV.

Below is the link and code :

Simple Digit Recognition OCR in OpenCV-Python

Implement it for alphabets. Hopes it works.

Answer 2

You can try building a model by uploading your training data (~50 images of 1s,2s,3s....9s) to demo.nanonets.ai (free to use)

1) Upload your training data here:

demo.nanonets.ai

2) Then query the API using the following (Python Code):

import requests
import json
import urllib
model_name = "Enter-Your-Model-Name-Here"
url = "http://images.clipartpanda.com/number-one-clipart-847-blue-number-one-clip-art.png"
files = {'uploadfile': urllib.urlopen(url).read()}
url = "http://demo.nanonets.ai/classify/?appId="+model_name
r = requests.post(url, files=files)
print json.loads(r.content)

3) the response looks like:

{
  "message": "Model trained",
  "result": [
    {
      "label": "1",
      "probability": 0.95
    },
    {
      "label": "2",
      "probability": 0.01
    },

     ....

    {
      "label": "9",
      "probability": 0.005
    }
  ]
}