Juggling between coding languages? Let our Code Converter help. Your one-stop solution for language conversion. Start now!
Disclosure: This post may contain affiliate links, meaning when you click the links and make a purchase, we receive a commission.
Humans can easily understand the text content of an image simply by looking at it. However, it is not the case for computers. They need some sort of structured method or algorithm to be able to understand it. This is where Optical Character Recognition (OCR) comes into play.
Optical Character Recognition is the process of detecting text content on images and converting it to machine-encoded text that we can access and manipulate in Python (or any programming language) as a string variable. In this tutorial, we gonna use the Tesseract library to do that.
Tesseract library contains an OCR engine and a command-line program, so it has nothing to do with Python, please follow their official guide for installation, as it is a required tool for this tutorial.
We gonna use pytesseract module for Python which is a wrapper for the Tesseract-OCR engine, so we can access it via Python.
The most recent stable version of Tesseract is 4 which uses a new recurrent neural network (LSTM) based OCR engine that is focused on line recognition.
Let's get started, you need to install:
pip3 install pytesseract
pip3 install numpy matplotlib opencv-python pillow
After you have everything installed on your machine, open up a new Python file and follow along:
import pytesseract import cv2 import matplotlib.pyplot as plt from PIL import Image
For demonstration purposes, I'm gonna use this image for recognition:
I've named it
"test.png" and put it in the current directory, let's load this image:
# read the image using OpenCV image = cv2.imread("test.png") # or you can use Pillow # image = Image.open("test.png")
As you may notice, you can load the image either with OpenCV or Pillow, I prefer using OpenCV as it enables us to use the live camera.
Let's recognize that text:
# get the string string = pytesseract.image_to_string(image) # print it print(string)
Note: If the above code raises an error, please consider adding Tesseract-OCR binaries to PATH variables. Read their official installation guide more carefully.
image_to_string() function does exactly what you expect, it converts the containing image text to characters, let's see the result:
This is a lot of 12 point text to test the ocr code and see if it works on all types of file format. The quick brown dog jumped over the lazy fox. The quick brown dog jumped over the lazy fox. The quick brown dog jumped over the lazy fox. The quick brown dog jumped over the lazy fox.
Excellent, there is another function
image_to_data() which outputs more information than that, including words with their corresponding
y coordinates, this will enable us to make a lot of useful stuff. For instance, let's search for words in the document and draw a bounding box around a specific word of our choice, below code, handles that:
# make a copy of this image to draw in image_copy = image.copy() # the target word to search for target_word = "dog" # get all data from the image data = pytesseract.image_to_data(image, output_type=pytesseract.Output.DICT)
So we're going to search for the word
"dog" in the text document, we want the output data to be structured and not a raw string, that's why I passed
output_type to be a dictionary, so we can easily get each word's data (you can print the
data dictionary to see how the output is organized).
Let's get all the occurrences of that word:
# get all occurences of the that word word_occurences = [ i for i, word in enumerate(data["text"]) if word.lower() == target_word ]
Now let's draw a surrounding box on each word:
for occ in word_occurences: # extract the width, height, top and left position for that detected word w = data["width"][occ] h = data["height"][occ] l = data["left"][occ] t = data["top"][occ] # define all the surrounding box points p1 = (l, t) p2 = (l + w, t) p3 = (l + w, t + h) p4 = (l, t + h) # draw the 4 lines (rectangular) image_copy = cv2.line(image_copy, p1, p2, color=(255, 0, 0), thickness=2) image_copy = cv2.line(image_copy, p2, p3, color=(255, 0, 0), thickness=2) image_copy = cv2.line(image_copy, p3, p4, color=(255, 0, 0), thickness=2) image_copy = cv2.line(image_copy, p4, p1, color=(255, 0, 0), thickness=2)
Saving and showing the resulting image:
plt.imsave("all_dog_words.png", image_copy) plt.imshow(image_copy) plt.show()
Take a look at the result:
Amazing, isn't it? This is not all! you can pass
lang parameter to
image_to_data() functions to make it easy to recognize text in different languages. You can also use the
image_to_boxes() function which recognizes characters and their box boundaries, please refer to their official documentation and available languages for more information.
A note though, this method is ideal for recognizing text in scanned documents and papers. Other uses of OCR include the automation of passport recognition and extraction of information from them, data entry processes, detection and recognition of car number plates, and much more!
Also, this won't work very well on hand-written text, complex real-world images, and unclear images or images that contain an exclusive amount of text.
Alright, that's it for this tutorial, let us see what you can build with this utility!
We have made a tutorial where you can use OCR to extract text from images inside PDF files, check it out!
Finally, I've collected some useful resources and courses for you for further learning, I highly recommend the following courses:
Happy Coding ♥
Ready for more? Dive deeper into coding with our AI-powered Code Explainer. Don't miss it!View Full Code Transform My Code