Extract Text from Image | Computer Vision | OpenCV | Project | Tesseract | Code Warriors
Extract Text from Image using Tesseract OCR with Python
In today’s Video we learned how to apply the Tesseract OCR engine with the Python programming language. This enabled us to apply OCR algorithms from within our Python script.
The biggest downside is with the limitations of Tesseract itself. Tesseract works best when there are extremely clean segmentations of the foreground text from the background.
Furthermore these segmentations need to be as high resolution (DPI) as possible and the characters in the input image cannot appear “pixelated” after segmentation. If characters do appear pixelated then Tesseract will struggle to correctly recognize the text — we found this out even when applying images captured under ideal conditions (a PDF screenshot).
OCR, while no longer a new technology, is still an active area of research in the computer vision literature especially when applying OCR to real-world, unconstrained images. Deep learning and Convolutional Neural Networks (CNNs) are certainly enabling us to obtain higher accuracy, but we are still a long way from seeing “near perfect” OCR systems. Furthermore, as OCR has many applications across many domains, some of the best algorithms used for OCR are commercial and require licensing to be used in your own projects.
Tesseract works best when there is a (very) clean segmentation of the foreground text from the background. In practice, it can be extremely challenging to guarantee these types of segmentations.
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