Optical Character Recognition (OCR) is a technology that enables computers to recognize and extract text from images or scanned documents. It has various applications such as digitizing printed documents, automated data entry, and language translation. In this blog post, we will explore how to implement OCR using C++.

Table of Contents

1. Introduction to OCR
2. Setting up Tesseract OCR
3. Performing OCR on an Image
4. Extracting Text from Scanned Documents
5. Conclusion

Introduction to OCR

OCR works by analyzing the shapes and patterns of characters in an image and converting them into machine-readable text. To perform OCR using C++, we can leverage the Tesseract OCR engine. Tesseract is an open-source library developed by Google, known for its accuracy and language support.

Setting up Tesseract OCR

To get started, we need to set up Tesseract OCR on our system. Follow these steps:

1. Download the Tesseract OCR library from the official GitHub repository.
2. Install the necessary dependencies, such as Leptonica and the training data for the desired languages.
3. Build and compile the Tesseract OCR library using CMake or your preferred build system.

Once you have successfully installed Tesseract OCR, we can move on to performing OCR on an image.

Performing OCR on an Image

To demonstrate OCR using C++, let’s consider an example where we have an image containing text and we want to extract that text programmatically.

We’ll first include the necessary header files:

#include <tesseract/baseapi.h>
#include <leptonica/allheaders.h>

Next, we’ll initialize the Tesseract OCR engine and set the language of the text we want to recognize:

tesseract::TessBaseAPI ocr;
ocr.Init(NULL, "eng");  // Set the language to English

We’ll load the image into a Pix object (a data structure used by Leptonica):

Pix *image = pixRead("path/to/image.png");
ocr.SetImage(image);

Now, we can perform OCR on the image and obtain the recognized text as a result:

char *text = ocr.GetUTF8Text();
cout << "Recognized Text:\n" << text << endl;

Finally, don’t forget to free the allocated memory:

ocr.End();
delete[] text;
pixDestroy(&image);

Extracting Text from Scanned Documents

In addition to OCR on single images, Tesseract OCR also provides functionality to handle multi-page documents. By processing each page sequentially, we can extract text from scanned documents in bulk.

To handle multiple pages, we need to call the SetPageSegMode method on the TessBaseAPI object and pass the appropriate page segmentation mode. For example:

ocr.SetPageSegMode(tesseract::PSM_AUTO);  // Automatic page segmentation mode

We can then iterate over each page and extract the recognized text:

tesseract::ResultIterator* iterator = ocr.GetIterator();
tesseract::PageIteratorLevel level = tesseract::RIL_TEXTLINE;

while (iterator->Next(level))
{
    char *text = iterator->GetUTF8Text(level);
    cout << "Recognized Text:\n" << text << endl;

    delete[] text;
}

Conclusion

In this blog post, we explored how to implement Optical Character Recognition (OCR) using C++. We learned how to set up the Tesseract OCR engine, perform OCR on individual images, and extract text from scanned documents. OCR opens up possibilities for automating data extraction and analysis from printed documents, improving productivity and efficiency.

By combining the power of C++ and OCR, you can develop innovative applications that can process and understand text from various sources. Happy coding!

#ocr #opticalcharacterrecognition