Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/nishant2018/text-extraction-ocr-opencv

Text extraction is the process of automatically extracting text from images or documents. Optical Character Recognition (OCR) is a technology that enables computers to convert images of text into machine-readable text.
https://github.com/nishant2018/text-extraction-ocr-opencv

ocr opencv python text-extraction

Last synced: 3 days ago
JSON representation

Text extraction is the process of automatically extracting text from images or documents. Optical Character Recognition (OCR) is a technology that enables computers to convert images of text into machine-readable text.

Awesome Lists containing this project

README 

        
## Text Extraction with OCR (Optical Character Recognition) using OpenCV



### Introduction



Text extraction is the process of automatically extracting text from images or documents. Optical Character Recognition (OCR) is a technology that enables computers to convert images of text into machine-readable text. In this tutorial, we'll explore how to perform text extraction using OCR with OpenCV, a popular computer vision library in Python.



### Why Use OCR for Text Extraction?



- **Automated Data Entry**: OCR allows for automated extraction of text from scanned documents, images, or screenshots, reducing the need for manual data entry.

- **Document Digitization**: OCR enables the digitization of printed documents, making them searchable and editable.

- **Text Recognition in Images**: OCR can be used to extract text from images captured by cameras or mobile devices, enabling applications like automatic license plate recognition and text translation.



### Key Steps in OCR Text Extraction with OpenCV



1. **Preprocessing**: Preprocess the input image to enhance the text and remove noise. Common preprocessing techniques include resizing, binarization, noise reduction, and contrast enhancement.

2. **Text Detection**: Use techniques such as edge detection, contour detection, or deep learning-based methods to locate regions containing text in the image.

3. **Text Recognition**: Apply OCR algorithms to recognize and extract text from the detected regions. Tesseract is a popular open-source OCR engine that can be integrated with OpenCV for text recognition.

4. **Post-processing**: Clean up and refine the extracted text to improve accuracy. This may involve removing artifacts, performing spell-checking, and formatting the text.



### Getting Started with OCR and OpenCV



1. **Install OpenCV and Tesseract**: Install OpenCV and Tesseract OCR on your system using package managers like pip or conda.

2. **Load Image**: Load the input image containing the text you want to extract using OpenCV's imread function.

3. **Preprocess Image**: Apply preprocessing techniques such as resizing, grayscale conversion, and thresholding to enhance the text.

4. **Text Detection**: Implement text detection algorithms to locate text regions in the preprocessed image.

5. **Text Recognition**: Use Tesseract OCR or other OCR libraries to recognize and extract text from the detected regions.

6. **Post-processing**: Clean up the extracted text and perform any necessary formatting or correction.



### Example Code



Here's a simple example of text extraction using OCR with OpenCV and Tesseract in Python:



```python

import cv2

import pytesseract



# Load image

image = cv2.imread('image.jpg')



# Preprocess image

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]



# Perform OCR

text = pytesseract.image_to_string(thresh)



# Print extracted text

print("Extracted Text:")

print(text)