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Hey, author of Zerox here! Happy to hear you saw good results. Given both libraries are primarily using GPT-4o-mini, I'd expect pretty similar performance. The

Hey, author of Zerox here! Happy to hear you saw good results. Given both libraries are primarily using GPT-4o-mini, I'd expect pretty similar performance. The author here seems to have some better prompts than we do: https://github.com/Dicklesworthstone/llm_aided_ocr/blob/main/llm_aided_ocr.py#L434C5-L434C26 https://github.com/Dicklesworthstone/llm_aided_ocr/blob/main... In our approach, we're just zero shot asking for markdown from the image. Vs this approach of passing in the Tesseract result + image context and asking for correction. I'm curious if there is a meaningful accuracy difference. My first thought it that the tesseract result may decrease accuracy, especially with tables or multi column pdfs. The tesseract model has a tendency to take everything from a table and throw it into one text blob. So while it's added context to the model, it's often not helpful.

jdthedisciple 2y ago
Hey there! Intuitively I would be surprised if the tesseract-way was better to be honest. I'm not the most experienced with OCR but I have tried Tesseract in the past and had difficulties. Like others mentioned, it would mix up things like 4 vs A etc. and had massive issues with decimal numbers. I have had zero such issues with zerox. Someone on HN also recently mentioned that tesseract is OCR of the past and basically outlived by a long shot already, I feel like that might be true. Btw I feel extremely lucky to have found Zerox because we needed a solution like that at work and so I introduced it and spun up a prototype to solve our problem and it works so well (combined with the brand new "strict json" feature by openai), so really great job making it!