These days people typically use a Convolutional Neural Network (CNN) to recognize an image that contains a single character. Talking about the Tesseract 4.00, it has a configured text line recognizer in its new neural network subsystem. Below is the visual representation of the Tesseract OCR architecture as represented in the Voting-Based OCR System research paper. It is used to recognize text from a large document, or it can also be used to recognize text from an image of a single text line. In this blog, I’ll be using the Python wrapper named by tesseract. It is through wrappers that Tesseract can be made compatible with different programming languages and frameworks.
The best part is that it supports an extensive variety of languages. You can use it directly or can use the API to extract the printed text from images. In the year 2006, Tesseract was considered as one of the most accurate open-source OCR engines.
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Tesseract is an open-source text recognition engine that is available under the Apache 2.0 license and its development has been sponsored by Google since 2006. This blog majorly focuses on the OCR’s application areas using Tesseract OCR, OpenCV, installation & environment setup, coding, and limitations of Tesseract. Automating the task of extracting text from images will help you to maintain and to analyze records. And just like always, with automation, you can take this to the next level. This time I am going to elaborate more on OCR especially about extracting information from an image. As promised to my readers, I am back with my second blog.
In my previous blog, I explained the basics of OCR and 3 important things that you should be aware of about OCR. And this is exactly where Optical Character Recognition comes in the picture. They only understand information that is organized. However, computers don’t function similarly. You can recognize the text on the image and can understand it without much difficulty. It is easy for humans to understand the contents of an image by just looking at it.
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