PYTHON TEXT SCANNER TUTORIAL INSTALL
PYTHON TEXT SCANNER TUTORIAL CODE
PYTHON TEXT SCANNER TUTORIAL DOWNLOAD

Here is the output of the EasyOCR module:, ,, ], The framework includes image preprocessing, deep learning model recognition, and image postprocessing. Figure 2 shows the operations in the EasyOCR framework. The im_1_path image took around ten seconds to be executed by recognize_text(). Note that it is going to take more time to execute EasyOCR on a CPU instead of a GPU. The returned OCR result is passed as the output of the recognize_text() function. The readtext method reads an image given its stored directory. For this tutorial, we want to only recognize English text, thus the ‘en’ in the list. It takes a list of languages as a parameter.

PYTHON TEXT SCANNER TUTORIAL CODE

'''loads an image and recognizes text.'''ĭid it surprise you that two lines of code are all you need to perform OCR? “Easy” for EasyOCR! The recognize_text() function initializes an OCR reader to a variable named reader. Use the reader to read an image with the following function: def recognize_text(img_path): Next, initialize an EasyOCR reader with a list of languages you would like to use. Define the path of an image using the following code: im_1_path = './folder/image_name.jpg'

PYTHON TEXT SCANNER TUTORIAL DOWNLOAD

Feel free to download them from or use your images. Step 2: Load Images and Extract Text using EasyOCRįor copyright reasons, all images used in the sample notebook are not provided in the GitHub repo. You’re off to a great start! Now, onto the next step. It sets the backend of the Matplotlib module to display figures inline and not on a separate window.

Note that the %matplotlib inline magic command is exclusive for Jupyter notebooks. At this point, you should be able to execute the following lines of code in your notebook: import cv2

PYTHON TEXT SCANNER TUTORIAL INSTALL

Now, go ahead and install the EasyOCR module - the tool we need for extracting text from images. You can follow the tutorial in our interactive Jupyter notebook online:įigure 1: Customized PyTorch installation command. For this tutorial, we will need OpenCV, Matplotlib, Numpy, PyTorch, and EasyOCR modules. An easy task for humans, but more work for computers to identify text from image pixels. Optical character recognition is a process of reading text from images.

Step 1: Install and Import Required Modules

Third, we will use OpenCV to overlay detected texts on the original images.

Second, we will perform image-to-text processing using EasyOCR on various images.

First, we will install the required libraries.

In this article, we will go through a three-step tutorial. It is a new, deep learning-based module for reading text from all kinds of images in more than 80 languages. You may ask: Is there any alternative that is as good as PyTesseract for OCR? Yes, EasyOCR it is. It uses classical computer vision methods to perform optical character recognition (OCR), then features neural network components such as LSTM from its fourth version onwards. First released in 2007, PyTesseract is the to-go library for extracting text from images.

Python text scanner tutorial