Text extraction is a process of extracting data or information from different sources such as images, scanned documents, invoices, bank statements, etc. This can be a routine task for business professionals, and sometimes for common individuals as well.
Although, there are numerous techniques and methods available that are being leveraged for text extraction. However, in this blog post, we will be discussing how it can be accurately performed using Python.
Python is a high-level programming language that is widely used for the creation of tools, websites, etc. But today, you will uncover it’s another capability.
A Step-by-Step Guide for Performing Text Data Extraction Using Python
Extracting text or data from different sources like images, receipts, etc. using Python requires following the right steps that we have discussed below in complete detail.
First Download & Install the Text Extraction Libraries:
The first step for you is to download and install essential Python libraries that will be responsible for performing text extraction from the input picture or receipt.
Python libraries you need to install are:
- OpenCV – also known as CV2, is a popular Python library that is known for performing various computer tasks such as image processing, etc.
- Pytesseract – It is basically a tool or engine that is powered by Optical Character Recognition (OCR) technology to extract editable text from input images with maximum accuracy.
- Pillow (PIL) – a special library that provides image manipulation and analysis capabilities to Python.
- TextExtract – This is also a library that is capable of getting text from different sources including pictures.
You have to download all these libraries on your device (laptop or PC). To do so, you can refer to Python’s official website. When the downloading is done, complete the installation process using this prompt.
Import the Libraries into the Code Editor:
Now, it is time to import the installed libraries into the code editor you are using. The process is quite simple. For your maximum ease, below we have written the prompt that you can use for importing.
import pytesseract
from PIL import Image
Upload the Image:
Once you are done with library importing, you can proceed towards the image uploading process. Mention “Name” against which the required image is saved on your computer. You can also consider providing a complete address for ease of image location.
The prompt you need to write for image uploading is.
img = cv2.imread('image.jpg')
Preprocess the Required Image (Optional):
Preprocessing is a stage in the text extraction process that involves removing any sort of distortions, noises, etc. from the input image to ensure quick and accurate extraction. So, if you also want to do so with your input picture, then you can follow this step, otherwise ignore it.
The OpenCV library will come into play to preprocess the given image. It will first turn the uploaded picture in greyscale so that, other Python libraries (such as Pytesseract, etc.) can quickly differentiate between letters and characters.
If needed, you can also apply a threshold on the input image. This process involves creating a binary version of the given picture using black and white. Below, we have written the Python code that you need to write to perform image preprocessing.
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
Start Extraction
Finally, the step has arrived that we all are waiting for. Here the Pytesseract library will be in action. It will effectively extract all the text from the given image without compromising on accuracy. The command you will need to write is as follows:
text = pytesseract.image_to_string(image.jpg)
# Print the extracted text
print(text)
These are a few steps that you need to follow to perform text extraction using Python.
But keep in mind that a single mistake in the code (even a missing comma or colon) can lead to errors, so be careful while writing. Here, it would be great if you go for advanced tools like Imagetotext.info. Such tools are trained on Python algorithms and can help you automatically perform extraction within seconds. Having said that, below is a quick demonstration of the said tool.
Demonstration of the Python-Trained Imagetotext.info
To show you how well Python-coded tools can work, we gave Imagetotext.info the following image to extract text from.
Once the image was provided and the tool processed it, here’s the output we got:
As you can see, tools like Imagetotext.info can accurately extract text from images. So, if you can’t do it using OpenCV, Pytesseract, Pillow, and TextExtract libraries, you can go down this easy road.
Wrapping Up
Text data extraction is a hectic and time-consuming task if done manually. That’s not the case now, thanks to Python. This high-level programming can be used to extract text from images accurately. In this detailed blog post, we have explained the step-by-step procedure and code examples for maximum understanding.
