OCR of Handwritten digits | OpenCV (original) (raw)

Last Updated : 17 Jan, 2025

**OCR which stands for **Optical Character Recognition is a computer vision technique used to identify the different types of handwritten digits that are used in common mathematics. To perform OCR in OpenCV we will use the KNN algorithm which detects the nearest k neighbors of a particular data point and then classifies that data point based on the class type detected for n neighbors.

**Data Used

This data contains 5000 handwritten digits where there are 500 digits for every type of digit. Each digit is of 20x20 pixel dimensions. We will split the data such that 250 digits are for training and 250 digits are for testing for every class. Below is the implementation.

Python `

import numpy as np import cv2

Read the image

image = cv2.imread('digits1.png')

gray scale conversion

gray_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

We will divide the image

into 5000 small dimensions

of size 20x20

divisions = list(np.hsplit(i,100) for i in np.vsplit(gray_img,50))

Convert into Numpy array

of size (50,100,20,20)

NP_array = np.array(divisions)

Preparing train_data

and test_data.

Size will be (2500,20x20)

train_data = NP_array[:,:50].reshape(-1,400).astype(np.float32)

Size will be (2500,20x20)

test_data = NP_array[:,50:100].reshape(-1,400).astype(np.float32)

Create 10 different labels

for each type of digit

k = np.arange(10) train_labels = np.repeat(k,250)[:,np.newaxis] test_labels = np.repeat(k,250)[:,np.newaxis]

Initiate kNN classifier

knn = cv2.ml.KNearest_create()

perform training of data

knn.train(train_data, cv2.ml.ROW_SAMPLE, train_labels)

obtain the output from the

classifier by specifying the

number of neighbors.

ret, output ,neighbours, distance = knn.findNearest(test_data, k = 3)

Check the performance and

accuracy of the classifier.

Compare the output with test_labels

to find out how many are wrong.

matched = output==test_labels correct_OP = np.count_nonzero(matched)

#Calculate the accuracy. accuracy = (correct_OP*100.0)/(output.size)

Display accuracy.

print(accuracy)

`

Output

91.64

You can download the source code by clicking here.