How To Make a Synthesized Dataset To Fine-Tune Your Ocr

Author(s): Eivind Kjosbakken

Originally published on Towards AI.

Fine-tuning your OCR engine to your specific use case is required if you want to achieve state-of-the-art performance for your situation. Fine-tuning an OCR engine requires a large dataset, however, which is expensive to annotate. Instead, you can create your own fully synthetic dataset, requiring no annotating, which I will show you how to do in this article.

Learn to create a fully synthetic receipt dataset with this tutorial. OpenAI. (2024). ChatGPT [Large language model]. /g/g-2fkFE8rbu-dall-e

· Motivation· Making a baseline· Finding the bounding boxes in the original image· Loading the annotated data· Adding word labels· Creating synthetic receipts· Adding variation to the receipts ∘ Augmentation 1: Add brightness ∘ Augmentation 2: Add noise ∘ Augmentation 3: Add perspective change ∘ Augmentation 4: Add sharpening ∘ Augmentation 5: Add Gaussian blur ∘ Augmentation 6: Add contrast ∘ Augmentation 7: Add saturation· Apply the augmentations· Future work· Conclusion· References

My motivation for this article is the fact that I tried applying EasyOCR to read the text off some Norwegian receipts, but I found the results not to be satisfactory. The Levenshtein distance achieved on the off-the-shelf EasyOCR was around 75%, which is not good enough when it comes to extracting information from supermarket receipts…. Read the full blog for free on Medium.

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Published via Towards AI