Remember, the success of pre-processing heavily depends on the quality and nature of the input image. Img = cv2.erode(img, kernel, iterations=1) Img = cv2.dilate(bin_img, kernel, iterations=1) # Perform dilation and erosion to remove some noise # C - It is just a constant which is subtracted from the mean or weightedīin_img = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2) # BLOCK Size - It decides the size of neighbourhood area. # neighbourhood values where weights are a gaussian window. # ADAPTIVE_THRESH_GAUSSIAN_C: threshold value is the weighted sum of # Use adaptive thresholding to convert the image to binary Here's a Python script using OpenCV for pre-processing an image before extracting text with Pytesseract: # Import the necessary libraries Deskewing: If the text in the image is skewed, straightening it can improve OCR results.Dilation and Erosion: These operations can help increase the text size and remove noise.This can help to make the text more distinguishable from the background. Binarization (Thresholding): Binarization is the process of converting an image to black and white.Noise can be reduced using techniques like Gaussian blur. Noise Removal: Images often contain noise, which can interfere with OCR.The quality of the extracted text using Pytesseract can be improved by applying several pre-processing steps to the image. How to improve the quality of the image for Pytesseract to better extract the text? Depending on the quality of your image and the type of text it contains, you might need to use some of these more advanced techniques. OpenCV offers a wide variety of functions for image processing which can significantly improve OCR results, such as binarization, noise removal, and more. This is a very basic image-processing step. Grayscale images can be more accessible for OCR tools to interpret, so this can improve the quality of text extraction. In this script, cv2.imread is used to open the image, and cv2.cvtColor is used to convert the image to grayscale. Gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) Here is an example Python script using P ytesseract and OpenCV: # Import the necessary libraries You can install the OpenCV Python library using pip: pip install opencv-python You can use it to read the image and apply some pre-processing such as converting the image to grayscale, noise removal etc. OpenCV (Open Source Computer Vision Library) is another great tool for image processing. There are many ways to preprocess images to potentially increase the quality of the OCR, but those are beyond the scope of this basic tutorial. The quality of the text extraction can decrease with low-quality photos or images with stylized text. ![]() Keep in mind that Tesseract works best on high-resolution images with clear, legible text. The extracted text is then printed to the console. This script opens an image file, then uses Pytesseract to extract any text it can find in the image. # Use pytesseract to convert the image data to text # replace 'test.png' with your image file # If you're on windows, you will need to point pytesseract to the path Now that all the required tools are installed, here's a simple script to extract text from an image: # Import the necessary libraries ![]() You can install these using pip: pip install pytesseract pillow ![]() Remember the path where you installed it, because you'll need to use it in your Python script.Īfter Tesseract is installed, you can now install the required Python libraries: pytesseract, and PIL (Python Imaging Library). You can download the executable from this link: and install it. Sudo apt install tesseract-ocr sudo apt install libtesseract-dev It is a command line tool that Python can access through a wrapper called Tesseract.īefore you can use Pytesseract, you will need to install Tesseract itself. However, Tesseract is not a Python library. A popular Python library for performing OCR is Tesseract. In order to extract text from an image, you can use a technique called Optical Character Recognition (OCR).
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