My task today for LLMs was "can you tell if this MRI brain scan is facing the normal way", and the answer was: no, absolutely not. Opus 4.1 succeeds more than chance, but still not nearly often enough to be useful. They all cheerfully hallucinate the wrong answer, confidently explaining the anatomy they are looking for, but wrong. Maybe Gemini 3 will pull it off. Now, Claude did vibe code a fairly accurate solution t…
But these models are more like generalists no? Couldn’t they simply be hooked up to more specialized models and just defer to them the way coding agents now use tools to assist?
A new Google model is nearly perfect on automated handwriting recognition
231–240 of 328 posts
Re: A new Google model is nearly perfect on automated handwriting recognition
#232Re: A new Google model is nearly perfect on automated handwriting recognition
#233Earlier quoted context omitted.
The comment above seems to violate several HN guidelines. Curious, I asked GPT and Gemini which ones stood out. Both replied with the same top three: https://news.ycombinator.com/newsguidelines.html They are: 1. “Be kind. Don't be snarky. … Edit out swipes.” 2. “Please don't sneer, including at the rest of the community.” 3. “Please don't post shallow dismissals, especially of other people's work. A good critical com…
I'd be interested in seeing these guidelines updated to include "don't re-post the output of an LLM" to reduce comments of this sort. I don't really feel like comments with LLM output as the primary substance meet the bar of "thoughtful and substantive", and (ironically, in this instance) could actually be used as good example of shallow dismissal, since you, a human, didn't actually provide an opinion or take a stan…
I enjoy the recursiveness of it all. Perhaps I should have said it outright.
Re: A new Google model is nearly perfect on automated handwriting recognition
#234Earlier quoted context omitted.
The comment above seems to violate several HN guidelines. Curious, I asked GPT and Gemini which ones stood out. Both replied with the same top three: https://news.ycombinator.com/newsguidelines.html They are: 1. “Be kind. Don't be snarky. … Edit out swipes.” 2. “Please don't sneer, including at the rest of the community.” 3. “Please don't post shallow dismissals, especially of other people's work. A good critical com…
genuinely, why is your response to being curious to ask two different LLMs to explain something to you? the list of guidelines has 18 items in it. did you actually need them to interpret it? or is it, perhaps, you couldn’t resist a little sneering yourself?
Re: A new Google model is nearly perfect on automated handwriting recognition
#235> In tabulating the “errors” I saw the most astounding result I have ever seen from an LLM, one that made the hair stand up on the back of my neck. Reading through the text, I saw that Gemini had transcribed a line as “To 1 loff Sugar 14 lb 5 oz @ 1/4 0 19 1”. If you look at the actual document, you’ll see that what is actually written on that line is the following: “To 1 loff Sugar 145 @ 1/4 0 19 1”. For those unawa…
I implemented a receipt scanner to Google Sheet using Gemini Flash. The fact that it is ”intelligent" it's fine for some things. For example I created structured output schema that had a field "currency" with the 3 letter format (USD, EUR...). So I scanned a receipt from some shop in Jakarta and it filled that field with IDR (Indonesian Rupiah). It inferred that data because of the city name on the receipt. Would it…
Almost certainly yes.
Re: A new Google model is nearly perfect on automated handwriting recognition
#236The thinking models (especially OpenAI's o3) still seem to do by far the best at this task as they look across the document to see how the writer wrote certain letters where the word is more clear when it runs into confusing words. I built a whole product around this: https://DocumentTranscribe.com But I imagine this will keep getting better and that excites me since this was largely built for my own research!
Re: A new Google model is nearly perfect on automated handwriting recognition
#237I really hope they have because I’ve also been experimenting with LLMs to automate searching through old archival handwritten documents. I’m interested in the Conquistadors and their extensive accounts of their expeditions, but holy cow reading 16th century handwritten Spanish and translating it at the same time is a nightmare, requiring a ton of expertise and inside field knowledge. It doesn’t help that they were of…
>I’m interested in the Conquistadors and their extensive accounts of their expeditions, but holy cow reading 16th century handwritten Spanish and translating it at the same time is a nightmare, requiring a ton of expertise and inside field knowledge Completely off topic, but out of curiosity, where are you reading these documents? As a Spaniard I’m kinda interested.
Re: A new Google model is nearly perfect on automated handwriting recognition
#238> In tabulating the “errors” I saw the most astounding result I have ever seen from an LLM, one that made the hair stand up on the back of my neck. Reading through the text, I saw that Gemini had transcribed a line as “To 1 loff Sugar 14 lb 5 oz @ 1/4 0 19 1”. If you look at the actual document, you’ll see that what is actually written on that line is the following: “To 1 loff Sugar 145 @ 1/4 0 19 1”. For those unawa…
The article's assumption of how the model ended up "transcribing" "1 loaf of sugar u/145" as "1 loaf of sugar 14lb 5oz" seems very speculative. It seems more reasonable to assume that a massive frontier model knows something about loaves of sugar and their weight range, and in fact Google search's "AI overview" of "how heavy is a loaf of sugar" says the common size is approximately 14lb.
Re: A new Google model is nearly perfect on automated handwriting recognition
#239Earlier quoted context omitted.
There is plenty reason. This article is just one example of many. People bring it up because LLMs routinely do things we call reasoning when we see them manifest in other humans. Brushing it off as 'deep pattern prediction' is genuinely meaningless. Nobody who uses that phrase in that way can actually explain what they are talking about in a way that can be falsified. It's just vibes. It's an unfalsifiable conversati…
You seem to be ignoring two things... First, the obvious one, is that LLMs are trained to auto-regressively predict human training samples (i.e. essentially to copy them, without overfitting), so OF COURSE they are going to sound like the training set - intelligent, reasoning, understanding, etc, etc. The mistake is to anthropomorphize the model because it sounds human, and associate these attributes of understanding…
1. “LLMs just mimic the training set, so sounding like they understand doesn’t imply understanding.”
This is the magic argument reskinned. Transformers aren’t copying strings, they’re constructing latent representations that capture relationships, abstractions, and causal structure because doing so reduces loss. We know this not by philosophy, but because mechanistic interpretability has repeatedly uncovered internal circuits representing world states, physics, game dynamics, logic operators, and agent modeling. “It’s just next-token prediction” does not prevent any of that from occurring. When an LLM performs multi-step reasoning, corrects its own mistakes, or solves novel problems not seen in training, calling the behavior “mimicry” explains nothing. It’s essentially saying “the model can do it, but not for the reasons we’d accept,” without specifying what evidence would ever convince you otherwise. Imaginary distinction.
2. “Humans predict the world, but LLMs only predict text, so humans understand but LLMs don’t.”
This is a distinction without the force you think it has. Humans also learn from sensory streams over which they have no privileged insight into the generative process. Humans do not know the “real world”; they learn patterns in their sensory data. The fact that the data stream for LLMs consists of text rather than photons doesn’t negate the emergence of internal models. An internal model of how text-described worlds behave is still a model of the world.
If your standard for “understanding” is “being able to successfully predict consequences within some domain,” then LLMs meet that standard, just in the domains they were trained on, and today's state of the art is trained on more than just text.
You conclude that “therefore the LLM understands nothing.” But that’s an all-or-nothing claim that doesn’t follow from your premises. A lack of sensorimotor grounding limits what kinds of understanding the system can acquire; it does not eliminate all possible forms of understanding.
Wouldn't the birds that have the ability to navigate from the earth's magnetic field soon say humans have no understanding of electromagnetism ? They get trained on sensorimotor data humans will never be able to train on. If you think humans have access to the "real world" then think again. They have a tiny, extremely filtered slice of it.
Saying “it understands nothing because autoregression” is just another unfalsifiable claim dressed as an explanation.
Re: A new Google model is nearly perfect on automated handwriting recognition
#240I really hope they have because I’ve also been experimenting with LLMs to automate searching through old archival handwritten documents. I’m interested in the Conquistadors and their extensive accounts of their expeditions, but holy cow reading 16th century handwritten Spanish and translating it at the same time is a nightmare, requiring a ton of expertise and inside field knowledge. It doesn’t help that they were of…
I'm skeptical that they're actually capable of making something novel. There are thousands of hobby operating systems and video game emulators on github for it to train off of so it's not particularly surprising that it can copy somebody else's homework.