This repository contains code snippets demonstrating how to perform Optical Character Recognition (OCR) using Pytesseract, an OCR tool for Python. Pytesseract is a wrapper for Google's Tesseract-OCR Engine, which is widely used for extracting text from images.
Make sure you have the following dependencies installed: pytesseract PIL (Python Imaging Library) OpenCV (cv2)
You can install these dependencies using pip: pip install pytesseract pillow opencv-python matplotlib
Pytesseract provides various page segmentation modes for different types of text in images. These modes determine how the OCR engine should interpret and process the image. Here are the available segmentation modes:
0 Orientation and script detection (OSD) only.
1 Automatic page segmentation with OSD.
2 Automatic page segmentation, but no OSD, or OCR.
3 Fully automatic page segmentation, but no OSD. (Default)
4 Assume a single column of text of variable sizes.
5 Assume a single uniform block of vertically aligned text.
6 Assume a single uniform block of text.
7 Treat the image as a single text line.
8 Treat the image as a single word.
9 Treat the image as a single word in a circle.
10 Treat the image as a single character.
11 Sparse text. Find as much text as possible in no particular order.
12 Sparse text with OSD.
13 Raw line. Treat the image as a single text line bypassing hacks that are Tesseract-specific.
Pytesseract also supports different OCR Engine modes, which determine the underlying OCR engine to be used. Here are the available OCR Engine modes:
0 Legacy engine only.
1 Neural nets LSTM engine only.
2 Legacy + LSTM engines.
3 Default, based on what is available.
Description: This function extracts text from an image.
How it helps: It is the core function for extracting text from images. It allows you to easily convert images containing text into machine-readable text data.
Description: Extracts the bounding box coordinates of each recognized character in the image.
How it helps: Useful for tasks requiring character-level information such as text localization, extracting individual characters, or analyzing text layout.
Description: Performs OCR on the image and returns result data including bounding box coordinates, confidence scores, and other information for each recognized word or character.
How it helps: Provides detailed information about recognized text, including its position, size, and confidence level. This can be used for tasks such as text extraction, analysis, or validation.
Description: Performs Orientation and Script Detection (OSD) on the input image and returns a dictionary containing the detected script, orientation, and other information.
How it helps: Useful for detecting the orientation of text in images, especially when dealing with images containing text in different orientations or scripts.