Show HN: LLM-aided OCR – Correcting Tesseract OCR errors with LLMs
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Use LLMs to correct Tesseract OCR errors, improving accuracy and efficiency
Action Steps
- Run Tesseract OCR on a document to generate initial text output
- Use an LLM like GPT4o-mini or Claude3-Haiku to correct obvious OCR errors
- Break down the document into smaller chunks and submit them to the LLM API concurrently
- Implement a multi-stage process to refine the output, passing the output of each stage as input to the next
- Test and evaluate the corrected output to measure the improvement in accuracy
Who Needs to Know This
Developers and data scientists working with OCR technology can benefit from this approach to improve text recognition accuracy
Key Insight
💡 LLMs can be used to correct OCR errors, especially with affordable and fast API options like GPT4o-mini and Claude3-Haiku
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📚 Improve Tesseract OCR accuracy with LLMs! 🤖
Key Takeaways
Use LLMs to correct Tesseract OCR errors, improving accuracy and efficiency
Full Article
Almost exactly 1 year ago, I submitted something to HN about using Llama2 (which had just come out) to improve the output of Tesseract OCR by correcting obvious OCR errors [0]. That was exciting at the time because OpenAI's API calls were still quite expensive for GPT4, and the cost of running it on a book-length PDF would just be prohibitive. In contrast, you could run Llama2 locally on a machine with just a CPU, and it would be extremely slow, but "free" if you had a spare machine lying around. Well, it's amazing how things have changed since then. Not only have models gotten a lot better, but the latest "low tier" offerings from OpenAI (GPT4o-mini) and Anthropic (Claude3-Haiku) are incredibly cheap and incredibly fast. So cheap and fast, in fact, that you can now break the document up into little chunks and submit them to the API concurrently (where each chunk can go through a multi-stage process, in which the output of the first stage is passed into an
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