51–60 of 456 posts
Thinking of the OCR vendors who get replaced. Where might they go?
One thing I can think of is that AI could help the space industry take off. But wondering if there are any concrete examples of new jobs being created.
I work in fintech and we replaced an OCR vendor with Gemini at work for ingesting some PDFs. After trial and error with different models Gemini won because it was so darn easy to use and it worked with minimal effort. I think one shouldn't underestimate that multi-modal, large context window model in terms of ease-of-use. Ironically this vendor is the best known and most successful vendor for OCR'ing this specific ty…
I've been fighting trying to chunk SEC filings properly, specifically surrounding the strange and inconsistent tabular formats present in company filings. This is giving me hope that it's possible.
I've tried Adobe Acrobat AI for this and it doesn't work yet. NotebookLM is it. The grounding is the reason it works - you can easily click on anything and it will take you to the source to verify it. My only gripe is that the visual display of the source material is _dogshit ugly_, like exceptionally so. Big blog pink background letters in lines of 24 characters! :) It has trouble displaying PDF columns, but at least it parses them. The ugly will change I'm sure :)
My projects are setup to let me bridge the gaps between the various sources and synthesize something more. It helps to have a goal and organize your sources around that. If you aren't focused, it gets confused. You lay the groundwork in sources and it helps you reason. It works so well I feel _tender_ towards it :) Survey papers provide background then you add specific sources in your area of focus. You can write a profile for how you would like NotebookLM to think - which REALLY helps out.
They are:
* The Stratigrapher - A Lovecraftian short story about the world's first city. All of Seton Lloyd/Faud Safar's work on Eridu. Various sources on Sumerian culture and religion All of Lovecraft's work and letters. Various sources about opium Some articles about nonlinear geometries
* FPGA Accelerated Graph Analytics An introduction to Verilog Papers on FPGAs and graph analytics Papers on Apache Spark architecture Papers on GraphFrames and a related rant I created about it and graph DBs A source on Spark-RAPIDS Papers on subgraph matching, graphlets, network motifs Papers on random graph models
* Graph machine learning notebook without a specific goal, which has been less successful. It helps to have a goal for the project. It got confused by how broad my sources were.
I would LOVE to share my projects with you all, but you can only share within a Google Workspaces domain. It will be AWESOME when they open this thing up :)
90% accuracy +/- 10%? What could that be useful for, that’s awfully low.
I work in fintech and we replaced an OCR vendor with Gemini at work for ingesting some PDFs. After trial and error with different models Gemini won because it was so darn easy to use and it worked with minimal effort. I think one shouldn't underestimate that multi-modal, large context window model in terms of ease-of-use. Ironically this vendor is the best known and most successful vendor for OCR'ing this specific ty…
What hardware are you using to run it?
Why is Gemini Flash so much cheaper than other models here?
I work in fintech and we replaced an OCR vendor with Gemini at work for ingesting some PDFs. After trial and error with different models Gemini won because it was so darn easy to use and it worked with minimal effort. I think one shouldn't underestimate that multi-modal, large context window model in terms of ease-of-use. Ironically this vendor is the best known and most successful vendor for OCR'ing this specific ty…
This is what I am trying to figure out how to solve. My problem statement is: - Injest PDFs, summarize, and extract important information. - Have some way to overlay the extracted information on the pdf in the UI. - User can provide feedback on the overlaid info by accepting or rejecting the highlights as useful or not. - This info goes back in to the model for reinforced learning. Hoping to find something that can m…
Most PDF parsers give you coordinate data (bounding boxes) for extracted text. Use these to draw highlights over your PDF viewer - users can then click the highlights to verify if the extraction was correct. The tricky part is maintaining a mapping between your LLM extractions and these coordinates. One way to do it would be with two LLM passes: 1. First pass: Extract all important information from the PDF 2. Second…
https://github.com/getomni-ai/zerox/pull/44
Related to
I work in fintech and we replaced an OCR vendor with Gemini at work for ingesting some PDFs. After trial and error with different models Gemini won because it was so darn easy to use and it worked with minimal effort. I think one shouldn't underestimate that multi-modal, large context window model in terms of ease-of-use. Ironically this vendor is the best known and most successful vendor for OCR'ing this specific ty…
I've been fighting trying to chunk SEC filings properly, specifically surrounding the strange and inconsistent tabular formats present in company filings. This is giving me hope that it's possible.