TrOCR Transformer-based OCR for Handwritten Text using Python
Ever wondered how AI models can truly 'read' an image and extract the text within, with human-like accuracy?
To try and get to the bottom of that, we'll be talking about TrOCR and its capabilities. Unlike historic or traditional OCR models, TrOCR leverages the power of modern transformers, specifically, it combines a vision transformer, similar to BEiT, for encoding the image, with a text transformer, similar to RoBERTa, for decoding it into readable text. This process is accomplished with a separate encoder-decoder architecture. TrOCR is specifically tailored for optical character recognition (OCR), its goal is to accurately transcribe text from images. In this video, I'll show you how to harness this powerful technology in a Python project, and transform images containing handwriting, into text, with just a few lines of code.
TrOCR provides an end-to-end approach, using a pre-trained image transformer encoder for input and text Transformer decoder for output. This diagram shows a simple summary of how the model takes an input image, shown on the lower right, breaks the image up into several patches or sections, then the patches are flattened and processed by the encoder to produce image embeddings. These embeddings are passed to the language transformer or decoder, which produces the output tokens. Finally the tokens are decoded into text. Feed-forward blocks and multi-head attention blocks are core elements of this transformer architecture. If you want to learn more, you can read the paper on TrOCR, link in the description below.
💻Code Repo: https://github.com/TechExpertTutorial...
💻Link to paper:
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models https://arxiv.org/pdf/2109.10282
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