The main problem with extracting tables from images is that the table data can be scattered all over the image, making it difficult to find and extract.
There is no built-in function in Python to extract tables from images but there are many libraries that you can use. One such library is pytesseract which is a wrapper for Google's Tesseract-OCR Engine.
Here is a simple example of how you can use pytesseract to extract table data from an image:
import pytesseract import cv2 # read the image image = cv2.imread("image.png") # convert the image to grayscale gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # apply thresholding to preprocess the image thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] # perform OCR on the thresholded image result = pytesseract.image_to_string(thresh) print(result)
This code imports the pytesseract and cv2 libraries.
Next, it reads in the image.
Then, it converts the image to grayscale.
After that, it applies thresholding to preprocess the image.
Finally, it performs OCR on the thresholded image and prints the result.
Work with images
There are a few ways to work with images in Python. The simplest way is to use the Image module, which provides a variety of methods for manipulating images. For example, you can create an image from scratch using the Image() function, or you can load an image from a file using the open() function.
Another way to work with images in Python is to use the PIL (Python Imaging Library) module. This module provides a variety of methods for manipulating images, including cropping and resizing them, converting them between different formats, and more.
Word with tables
In Python, you can create tables with the table() function. The table() function takes two arguments: the data type of the table and a list of column names. Here is an example:
table(“name”, “age”)
name age
John 20
Jane 25
