41–50 of 86 posts
I don't know why people mess with tesseract in 2026, attention-based OCRs (and more recently VLMs) outperformed any LSTM-based approach since at least 2020. My guess is that it's the entry-point to OCR and the internet is flooded by that, just like pandas for data processing.
Leaving a comment so I can more easily find this
And for the people wondering about Pandas, use Polars instead
https://en.wikipedia.org/wiki/Franchise_(short_story)
the protagonist is interviewed as a one-man "focus group" in lieu of a national election and one of the questions he is asked is "What do you think about the price of eggs?" and he said roughly "I have no idea, my wife does the shopping."
Overall this feels less like a quirky egg project and more like a blueprint for how messy real-world data pipelines are going to look going forward
...
>>These are the days of miracle and wonder. I can’t wait to see what [the next] 30 years of eggs looks like.
Earlier quoted context omitted.
That is very curious, yes. Eggs seem to just start to increase dramatically after 2000 and indeed outdo the CPI, disregarding the peaks and valleys of the different shocks to egg production like covid and the avian flu. I read that the price includes free range, eco, etc varieties which are more expensive and in more demand nowadays, probably just that explains a good chunk of the price increase.
This is a good read if you haven’t seen it. Spoiler alert it’s private equity. Shocker I know. https://www.thebignewsletter.com/p/hatching-a-conspiracy-a-b...
I usually avoid shallow comments but I feel like this time it has to be said as a conversation starter: That's a lot of eggs! Also ignoring the benefits of subscriptions, an estimate in the magnitude of thousands of dollars for extracting egg prices still makes me feel like we aren't "there" yet. This should have been a problem with a much more efficient solution given the advancements in the AI, data analysis and OC…
> This should have been a problem with a much more efficient solution given the advancements in the AI, data analysis and OCR space. There's got to be a "it's a chicken/egg problem" joke in there somewhere, but i'm not seeing it.
Inflation adjusted dsta just comes to tell us that either eggs have been outdoing the CPI for 25 years or that actual CPI is way higher than what the BLS calculates.
Or a third option: eggs are just a terrible proxy for CPI
Absolutely loved the article, the process, and the results. Hated the price. You could pay a human to read receipts, 1 every 30 seconds (that’s slow!), $15/hr (twice the US federal minimum wage!), plus tax and overhead ($15x1.35) comes out to $20.25/hr over 5 hours. $101 all in. Sure, sure, a human solution doesn’t scale. But this sort of project makes me feel like we haven’t hit the industrialization moment that i t…
Receipt scanning OCR has been around for a long time. Circa 2010 I ran enough HITs on Mechanical Turk [1] that I got my own account representative at AWS and I wondered what other kind of HITs other people were running and thought I would "go native" and try making $100 from Turk.
I am pretty good at making judgements for training sets, I have many times made data sets with 2,000-20,000 judgements; I can sustain the 2000 judgements/day of the median Freebase annotator and manage short burst much higher than that with mild perceptual side effects.
I gave up as a Turk though because the other HITs that were easy to find was the task of accurately transcribing cell phone snaps of mangled, damaged, crumpled, torn, poorly printed, poorly photographed or otherwise defective receipts. I can only imagine that these receipts had been rejected by a rather good classical OCR system. The damage was bad enough I could not honestly say I had done a 100% correct job on any single receipt, as I was being asked to do.
[1] in today's lingo: Multimodal with prompts like "Is this a photograph of an X?" and "Write a headline to describe this image"
Earlier quoted context omitted.
This is a good read if you haven’t seen it. Spoiler alert it’s private equity. Shocker I know. https://www.thebignewsletter.com/p/hatching-a-conspiracy-a-b...
That is indeed a good read, I wasn't aware that there is now a Big Egg fixing egg prices.
Absolutely loved the article, the process, and the results. Hated the price. You could pay a human to read receipts, 1 every 30 seconds (that’s slow!), $15/hr (twice the US federal minimum wage!), plus tax and overhead ($15x1.35) comes out to $20.25/hr over 5 hours. $101 all in. Sure, sure, a human solution doesn’t scale. But this sort of project makes me feel like we haven’t hit the industrialization moment that i t…
You're counting just the egg-having receipts, but there were over 11 thousand receipts they had to go through to get to that 500-ish subset. I'm assuming OP wanted to process all of the receipts and then selected just eggs for a simple analytics job. With your rates, the human would cost almost $2000.
Had the filtering been done during the initial document storage, then the cost would have been much cheaper than your $2,000 estimate. Essentially binning the receipts based on "eggs" or "no eggs" would be free. But, crucially, what happens when the question changes from price per egg to price per gallon of milk? Now the whole stack would need to be sorted again. The $2,000 manual classification would need to be re-applied.
Isn't traditional ML-based classification cheaper for this problem at industrial scale than an LLM though? The OP did of course attempt more traditional generic off-the-shelf OCR tools, but let's consider proper bespoke industrial ML.
Just as a off-the-cuff example, I would probably start with building a tool that locates the date/time from a receipt and takes an image snip of it. Running ONLY image snips through traditional OCR is more successful than trying to extract text from an entire receipt. I would then train a separate tool that extracts images of line items from a receipt that includes item name and price. Yet another tool could then be trained to classify items based on the names of the items purchased, and a final tool to get the price. Now you have price, item, and date to put into your database.
Perhaps generating the training data to train the item classifier is the only place I could see an LLM being more cost effective than a human, but classifying tiny image snips is not the same as one-shotting an entire receipt. As an aside, if there's any desire to discuss how expensive training ML is, don't forget the price to train an LLM as well.
All of this is to say I believe traditional ML is the solution. I'm still not seeing the value prop of LLMs at the industrialization scale outside of very targeted training data generation. A more flippant conclusion might be that we can replace a lot of the parts of data science that makes PhD types get bored with creating traditional ML solutions.