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About this Job
blockterms is a blockchain based finance and technology startup. We work with crypto currencies, payments and new ideas in
finance like smart contracts.
We are seeking an amazing front end and or back end developer to join our team.
This internship is for individuals that are excited to apply their learning to practice and at the same time gain a sense of
the start-up environment.
About this Job
blockterms is a blockchain based finance and technology startup.
We work with crypto currencies, payments and new ideas in finance like smart contracts.
We are seeking an amazing Community development intern to help us with marketing, communication and messaging to
blockchain audience around the world.
This internship is for individuals that are excited to apply their learning to practice and at the same time gain a
sense of the start-up environment.
About this Job
blockterms is a blockchain based finance and technology startup. We work with crypto currencies, payments and new ideas in
finance like smart contracts.
We are seeking an amazing front end and or back end developer to join our team.
This internship is for individuals that are excited to apply their learning to practice and at the same time gain a sense of
the start-up environment.
@yanilkr
yanilkr / README.md
Created March 24, 2016 18:24 — forked from dannguyen/README.md
Using Google Cloud Vision API to OCR scanned documents to extract structured data

Using Google Cloud Vision API's OCR to extract text from photos and scanned documents

Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.

The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.

On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:

####### 1. A low-resolution photo of road signs