Table of Contents
import boto3 | |
import base64 | |
if __name__ == '__main__': | |
session = boto3.session.Session() | |
kms = session.client('kms') | |
encrypted_password = 'AQECAHjgTiiE7TYRGp5Irf8jQ3HzlaQaHGYgsUJDaavnHcFm0gAAAGswaQYJKoZIhvcNAQcGoFwwWgIBADBVBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDDwxVQuG0oVwpkU7nQIBEIAoVGk1/wpserb+GVUOzE7PiL/Nr9fTDFKZfpKpF0ip2ct4B2q0Wn6ZZw==' | |
binary_data = base64.b64decode(encrypted_password) |
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
Picking the right architecture = Picking the right battles + Managing trade-offs
- Clarify and agree on the scope of the system
- User cases (description of sequences of events that, taken together, lead to a system doing something useful)
- Who is going to use it?
- How are they going to use it?
// sending to sender-client only | |
socket.emit('message', "this is a test"); | |
// sending to all clients, include sender | |
io.emit('message', "this is a test"); | |
// sending to all clients except sender | |
socket.broadcast.emit('message', "this is a test"); | |
// sending to all clients in 'game' room(channel) except sender |
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.