![]() It’s paramount that we obtain these bounding box coordinates. Once we have the table, we can apply OCR and text localization to generate the (x, y)-coordinates for the text bounding boxes. Right: Our goal is to detect and extract the table of data from the input image. Given this image, we then need to extract the table itself ( right).įigure 1: Left: Our input image containing statistics from the back of a Michael Jordan baseball card (yes, baseball - that’s not a typo). To start, we need to accept an input image containing a table, spreadsheet, etc. Our multi-column OCR algorithm is a multi-step process. Build a Pandas DataFrame from the table to process it, query it, etc.We’ll wrap up the lesson by applying our Python implementation to: We’ll spend most of the lesson here, covering our multi-column OCR algorithm’s details and inner workings. With our development environment fully configured, we can move on to our implementation. We’ll also install any additional required Python packages for the tutorial. It serves as a great starting point, and we recommend using it whenever you need to OCR a table.įrom there, we’ll review the directory structure for our project. This is the exact algorithm to OCR multi-column data. In the first part of this tutorial, we’ll discuss our multi-column OCR algorithm’s basic process. In this tutorial, you’ll learn some tips and tricks to OCR multi-column data, and most importantly, associate rows/columns of text together. The good news is that while OCR’ing multi-column data is certainly more demanding than other OCR tasks, it’s not a “hard problem,” provided you bring the right algorithms and techniques to the project. So, while OCR’ing multi-column data may appear to be an easy task, it’s far harder since we may need to be responsible for associating text into columns and rows - and as you’ll see, that is indeed the most complex part of our implementation. ![]() Your OCR engine (Tesseract, cloud-based, etc.) may correctly OCR the text but be unable to associate the text into columns/rows.You may need first to detect the table in the image before you can OCR it.Tesseract isn’t very good at multi-column OCR, especially if your image is noisy.But unfortunately, we have a few problems to address: In most cases, yes, that would be the case. On the surface, OCR’ing tables seems like it should be an easier problem, right? However, given that the document has a guaranteed structure, shouldn’t we leverage this a priori knowledge and then OCR each column in the table? Perhaps one of the more challenging applications of optical character recognition (OCR) is how to successfully OCR multi-column data (e.g., spreadsheets, tables, etc.). Looking for the source code to this post? Jump Right To The Downloads Section Multi-Column Table OCR ![]()
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