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Model weights are automatically downloaded or can be manually downloaded as well. This needs to run only once to load the necessary models. The Reader class is the base class for EasyOCR which contains a list of language codes and other parameters such as GPU that is by default set to True.
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Text Detection in Images with EasyOCRĮasyOCR can process multiple languages at the same time provided they are compatible with each other.
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Now we’re ready to start our detection process. EasyOCR doesn’t have much software dependencies, it can directly be used with its API. It is composed of 3 main components, feature extraction (we are currently using Resnet), sequence labelling ( LSTM) and decoding (CTC). The detection part is using the CRAFT algorithm and the Recognition model is CRNN. All About EasyOCRĮasyOCR is built with Python and Pytorch deep learning library, having a GPU could speed up the whole process of detection. Let’s start by discussing EasyOCR and installing it for our use.
#Do ocr devices read printed characters in an ocr font how to
In this article, we will be discussing how to implement OCR using EasyOCR. EasyOCR is created by the Jaided AI company. It is by far the easiest way to implement OCR and has access to over 70+ languages including English, Chinese, Japanese, Korean, Hindi, many more are being added. OCR is an emerging technology which is enhancing for better accuracy in performance.ĮasyOCR is a python package that allows the image to be converted to text. Converting these visuals to text could be handy for information extraction, scanning books or documents and making PDFs, storing in the system or working with it on-line such as text to speech(this could be of great help to visually impaired people), used widely in autonomous vehicles to interpret various things. These visuals could be printed documents(invoices, bank statements, restaurant bills), or placards(sign-boards, traffic symbols ), or handwritten text. Optical Character Reader or Optical Character Recognition (OCR) is a technique used to convert the text in visuals to machine-encoded text.