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Welcome back to LearnOpenCV! In this session, we are delving into enhancing TrOCR, a top-tier, transformer-based OCR model, to adeptly recognize curved text, a feat it initially struggled with. Make sure to catch up on our last video for a detailed introduction to TrOCR and its functionality!
📌 In today’s tutorial, we are focusing on fine-tuning the TrOCR small model, utilizing text images from the wild, specifically tackling those from the SCUT-CTW1500 dataset. Our primary aim is to bolster the model's proficiency in recognizing curved and vertical images and push the boundaries of what TrOCR models can achieve.
🚀 We will walk through the entire process, from preparing and analyzing the curved text images dataset to running inference post-training, using the innovative Hugging Face Trainer API. We will assess the upgraded model's capability and analyze the results to understand the extent of improvement in recognizing curved text.
💡 Stay tuned as we embark on this journey to unravel the possibilities of fine-tuning TrOCR models and explore their enhanced applications in real-world scenarios, contributing to the advancements in the OCR domain!
Steps Covered:
✅ 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.
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