Hello trend,
This is Satya Mallick from LearnOpenCV.com.
TrOCR (transformer-based Optical Character Recognition) models are some of the best-performing OCR models. Our previous article analyzed how well they perform on single-line printed and handwritten text. However, like any other deep learning model, they have their limitations. TrOCR does not perform well on curved text out of the box. This article will take the TrOCR series a step further by fine-tuning TrOCR model on a curved text dataset.
In today's article, We will use the Hugging Face Trainer API for training the model. To complete the entire process, the following steps must be followed:
- Prepare and analyze the curved text images dataset.
- Load the TrOCR Small Printed model from Hugging Face.
- Initialize the Hugging Face Sequence to Sequence Trainer API.
- Define the evaluation metric
- Train the model and run inference.
Training TrOCR to recognize curved text |
The accompanying code for the blog post can be found here:
Download Code |
If you want to learn more about Transformers, Text Detection, and OCR, I'm sharing a list of resources that might be helpful for you:
- Understanding TrOCR
- Optical Character Recognition using PaddleOCR
- Automatic License Plate Recognition using Deep Learning
- Understanding the Attention Mechanism in Transformers
- Implementing Vision Transformers in PyTorch
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Satya
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