Falcor
GraphQL
FastAPI
OData
LoopBack.io
Mercurius
Django REST framework
PostgREST
DocParser
Nanonets
Parseur.com
Rossum
Docsumo
DocuClipper
FlexiCapture
Parsio.io
Falcor DocParserBased on our record, DocParser should be more popular than Falcor. It has been mentiond 14 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Interesting the article jumps straight from REST to GraphQL and forgets Falcor[0] - Netflix's alternative vision for federated services. For a while it looked like it might be a contender to GraphQL but it never really seemed to take off despite being simpler to adopt. [0] https://netflix.github.io/falcor/. - Source: Hacker News / almost 3 years ago
- obviously netflix with falcor, EVCache and hundreds of other projects. Source: about 4 years ago
I pushed for Falcor over GraphQL in 2016. I still think Falcor was a more elegant core idea, but the implementation, tooling, and community never materialized like it did with GraphQL, and now Falcor is relatively niche and obscure. Netflix wasn't willing or able to promote it like Facebook did with GraphQL. That was beginning to be apparent in 2016, but I liked the concept too much. Source: almost 5 years ago
Netflix has two amazing aspects I think. One is obviously the movie infrastructure and the other the way they do data and state management. I would read up on https://netflix.github.io/falcor to get an idea what is involved here.to be honest I dont get the point of rebuilding the visual aspects of their web app, that part is trivial and also completely useless without the parts that matter. Source: about 5 years ago
You could try an online service like https://extract-io.web.app/ or https://docparser.com/. Source: about 3 years ago
DocParser: DocParser simplifies the extraction of structured data from various file formats, such as PDFs and scanned documents, directly into Google Sheets. By automating this process, DocParser saves valuable time and effort otherwise spent on manual data entry. Link to DocParser. Source: about 3 years ago
There are several tools available today that can help you extract tables from PDF files (such as Tabula), or even parse PDFs into structured JSON using AI (like Parsio -> I'm the founder) or without AI (like Docparser). Source: over 3 years ago
Thank you for sharing those! I didn't know them I've only checked this one https://docparser.com/ and I think my solution could be better because it will be easier for the user. Source: over 3 years ago
As previously suggested, if the layout of your PDFs never changes (consistent column widths in tables and placement), you can use a zonal PDF parser like DocParser. Alternatively, an AI-powered parser may be a better choice. Source: over 3 years ago
GraphQL - GraphQL is a data query language and runtime to request and deliver data to mobile and web apps.
Nanonets - Worlds best image recognition, object detection and OCR APIs. NanoNetsโ platform makes it straightforward and fast to create highly accurate Deep Learning models.
FastAPI - FastAPI is an Open Source, modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints.
Parseur.com - Automate text extraction from emails and PDFs by using our powerful email and document parser.
OData - OData, short for Open Data Protocol, is an open protocol to allow the creation and consumption of queryable and interoperable RESTful APIs in a simple and standard way.
Rossum - Rossum is AI-powered, cloud-based invoice data capture service that speeds up invoice processing 6x, with up to 98% accuracy. It can be easily customized, integrated and scaled according to your company needs.
