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Given that we can detect the writing system of the text, it raises the question: Is it possible to translate text from one language to another using OCR and Tesseract? To learn how to translate languages using Tesseract and Python,…
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This post covers the intuition of Generative Adversarial Networks (GANs) at a high level, the various GAN variants, and applications for solving real-world problems. This is the first post of a GAN tutorial series: Intro to Generative Adversarial Networks (GANs)…
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In our previous tutorial, you learned how to OCR only digits from an input image. But what if you wanted to obtain more fine-grained control on the character filtering process? For example, when building an invoicing application, you may want…
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In a previous tutorial, we implemented our very first OCR project. We saw that Tesseract worked well on some images but returned total nonsense for other examples. Part of being a successful OCR practitioner is learning that when you see…
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The first time I ever used the Tesseract optical character recognition (OCR) engine was in my college undergraduate years. A dataset comprising diverse textual images is necessary for an OCR project. It enables the OCR system to learn different text…
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In this tutorial, we will configure our development environment for OCR. Once your machine is configured, we’ll start writing Python code to perform OCR, paving the way for you to develop your own OCR applications. A text-image dataset is useful…
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Optical character recognition, or OCR for short, is used to describe algorithms and techniques (both electronic and mechanical) to convert images of text to machine-encoded text. We typically think of OCR in terms of software. Namely, these are systems that:…
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Previously, we discussed how to save and serialize your models to disk after training is complete. We also learned how to spot underfitting and overfitting as they are happening, enabling you to kill off experiments that are not performing well…
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Our entire review of machine learning and neural networks thus far has been leading up to this point: understanding Convolutional Neural Networks (CNNs) and the role they play in deep learning. In traditional feedforward neural networks, each neuron in the…
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