I've already provided fields of what information should be extracted, i.e., passport
- number
- name
- expiration
- country of issue
- place to attach a picture
I've created a series of templates for different types of documents (passports, driver's licenses, receipts, insurance policies) and I want to be able to scan the document and have the OCR 1) determine what template it should be on 2) extract relevant information to fill out the "form" aka template so the user doesn't have to
How do you find an item then? I've read numerous research studies that prove people still prefer navigation over search. Ofer Bergman has done a lot of work.
Indium tin oxide is a byproduct of the zinc oxide refinement process so it's doesn't see sustainable/enough to go around which seems like graphene is a better choice. Right now, they're primarily made of perovskite which is hybrid inorganic/organic and a lil toxic. MIT has a few studies on using graphene for OPV.
If I'm implementing search in an application and want to use NLP, do I need to train the search or are these solutions already ready to go? I'm not sure how other people do it/how search works/if you need to tell it what to do.
We're going into the next iteration because the first product was terribly unusable and I want it to be a more efficient process this time. I remember at one point we were arguing about validation in a text field and I came up with bananas explanations and to add to it, I thought I needed to point out every field in the entire application where it needed to be fixed - mortified to say the least, and I learned that the word is "validation" for what I was trying to describe.
Right now, I think I should keep it as a web app because it seems easier (?) but will need to add desktop and then mobile (maybe mobile before desktop for upload and portability reasons, but I haven't decided).