* The text mentions OCR, but the screenshots show documents with fidelity far beyond what I would expect via scanning. I would guess that the screenshots in actuality show PDFs that include character and layout information, i.e. they don't simply contain scanned images. If my guess is correct, why is OCR needed?
* How does segmentation contrast with layout analysis, or are they synonymous?
* I know a lot of work has been done on layout analysis in commercial off-the-shelf OCR software. How do these results (up to but obviously not including the summarization itself) compare? Or, how would you expect them to compare?
Thanks!
thanks for your questions! We have updated the post and included the answers to your questions.
- Of course, this step can be omitted if the documents already have text embeddings. However, it is often necessary to read tables or scanned documents, for example. In our software solution, the users can decide for any project if they want to use text embeddings, Tesseract, or a commercial OCR.
- With page segmentation or also called layout analysis, we refer to the division of a document into separate parts.
- This is done with our own trained model because we couldn’t achieve the needed outcome with off-the-shelf software like Tesseract or Abbyy FineReader.
Did you see this, posted earlier today? It looks like the actual data isn't available yet, however.
https://news.ycombinator.com/item?id=26339769
Wit: Wikipedia-Based Image Text Dataset (github.com/google-research-datasets)
Note: I think the "Register for free" button for the webinar is broken.
The page segmentation API is already live. The PDF summarization API is work in progress. We just wanted to share our approach already now to incorporate any feedback! We are also working on the retraining loop to fine-tune our model on a small sample of other documents. We support this for custom NER models and document classification so far.
Best Chris
