![]() ![]() The background is inconsistent - it is significantly lighter on the left-hand side versus darker on the right-hand side.As humans, we can easily see that this image contains the text “12-14,” but for a computer, it poses several challenges, including: In this tutorial, we will be OCR’ing the image from Figure 2. When Tesseract by Itself Fails to OCR an Image ![]() We’ll be using this same image to learn pre-processing and clean-up techniques using OpenCV to ensure OCR success with Tesseract. To combat this, we’ll develop an image processing script process_image.py that prepares the image for successful OCR with Tesseract.įigure 2: This image was posted on Stack Overflow and a number of solutions were discussed by community contributors. The challenging_example.png flat out doesn’t work with Tesseract as is (even with Tesseract v4’s deep learning capability). This project involves a challenging example image that I gathered on Stack Overflow ( Figure 2). Let’s get started by reviewing our project directory structure: |- challenging_example.png Gain access to Jupyter Notebooks for this tutorial and other PyImageSearch guides that are pre-configured to run on Google Colab’s ecosystem right in your web browser! No installation required.Īnd best of all, these Jupyter Notebooks will run on Windows, macOS, and Linux! Project Structure Then join PyImageSearch University today!
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