Extracting text from images with Tesseract OCR, OpenCV, and Python

It is easy for humans to understand the contents of an image by just looking at it. You can recognize the text on the image and can understand it without much difficulty. However, computers don’t function similarly. They only understand information that is organized. And this is exactly where Optical Character Recognition comes in the picture. In my previous blog, I explained the basics of OCR and 3 important things that you should be aware of about OCR. As promised to my readers, I am back with my second blog. This time I am going to elaborate more on OCR especially about extracting information from an image. And just like always, with automation, you can take this to the next level. Automating the task of extracting text from images will help you to maintain and to analyze records. This blog majorly focuses on the OCR’s application areas using Tesseract OCR, OpenCV, installation & environment setup, coding, and limitations of Tesseract. So, let’s begin.

Tesseract OCR

Talking about the Tesseract 4.00, it has a configured text line recognizer in its new neural network subsystem. These days people typically use a Convolutional Neural Network (CNN) to recognize an image that contains a single character. Text that has arbitrary length and a sequence of characters is solved using Recurrent Neural Network (RNNs) and Long short-term memory (LSTM) where LSTM is a popular form of RNN. The Tesseract input image in LSM is processed in boxes (rectangle) line by line that inserts into the LSTM model and gives the output.

By default, Tesseract considers the input image as a page of text in segments. You can configure Tesseract’s different segmentations if you are interested in capturing a small region of text from the image. You can do it by assigning –psm mode to it. Tesseract fully automates the page segmentation but it does not perform orientation and script detection. The different configuration parameters for Tesseract are mentioned below:

Page Segmentation Mode (–psm): By configuring this, you can assist Tesseract in how it should split an image in the form of texts. The command-line help has 11 modes. You can choose the one that works best for your requirement from the table given below…read more.

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