python-kerasHow do I use Python Keras to perform Optical Character Recognition (OCR)?

Optical Character Recognition (OCR) is a process of recognizing text from images. Python Keras can be used to perform OCR by first pre-processing the images to make the text as clear as possible and then using a convolutional neural network (CNN) to recognize the text in the images.

Example code

import cv2
import numpy as np
from keras.models import load_model

# Pre-processing the image
image = cv2.imread('image.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Load the model
model = load_model('model.h5')

# Recognize the text
prediction = model.predict(thresh.reshape(1, thresh.shape[0], thresh.shape[1], 1))
prediction = np.argmax(prediction, axis=2)[0]

The code above first pre-processes the image by converting it to grayscale and then applying a threshold to it. Then it loads the model and uses it to recognize the text in the image.

The code consists of the following parts:

1. import cv2 and import numpy as np: These are used to import the necessary libraries for the code.
2. image = cv2.imread('image.png'): This is used to read the image.
3. gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY): This is used to convert the image to grayscale.
4. thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]: This is used to apply a threshold to the grayscale image.
5. model = load_model('model.h5'): This is used to load the model.
6. prediction = model.predict(thresh.reshape(1, thresh.shape[0], thresh.shape[1], 1)): This is used to predict the text in the image.
7. prediction = np.argmax(prediction, axis=2)[0]: This is used to get the prediction from the model.

Helpful links

• https://www.pyimagesearch.com/2018/09/17/opencv-ocr-and-text-recognition-with-tesseract/
• https://www.pyimagesearch.com/2018/09/24/opencv-ocr-and-text-recognition-with-keras/

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