Hi,

This week you'll learn about Multi-Column Table OCR.

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Since its introduction, Optical Character Recognition (OCR) has taken the world by storm. OCR is presently employed in a variety of real-world applications, ranging from scanning license plates to automating checkbook transactions.

One of the main use cases of text recognition is to read tabular data from images. It's easy to read text data from images, but to read it in the exact orientation it is present in is a challenging problem.

Today, we'll learn to work with Multiple Column Table Optical Character Recognition. 

The big picture: We organize our world through structured tabular data. Our eyes can easily detect those from images. But making a computer detect tabular-oriented data will also involve estimating the region of interest (ROI) and keeping the orientation intact.

How it works: Using OpenCV's vast array of tools, we'll first estimate the general region of the table in the input image. After that, we will use PyTesseract to extract the text from the ROI and finally apply an algorithm known as Hierarchical Agglomerative Clustering (HAC) to get our output in its original intended form. 

Our thoughts: The HAC algorithm is the integral puzzle piece that connects text recognition with traditional structural data. 

Yes, but: The HAC algorithm isn't foolproof since it doesn't learn from data. 

Stay smart: Do not stop here. There can be multiple solutions to a single problem. We recommend you go through computer vision and deep learning topics and craft a solution to solve tabular data text detection.

Click here to read the full tutorial

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