dannguyen

Using Python 3 + 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

vasanthk

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

1. 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?

bishboria

These links no longer work. Springer have pulled the free plug.

Graduate texts in mathematics

duplicates = multiple editions

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

kristopolous

// Usage:
//   Copy and paste all of this into a debug console window of the "Who is Hiring?" comment thread 
//   then use as follows:
//
//   query(term | [term, term, ...], term | [term, term, ...], ...)
// 
// When arguments are in an array then that means an "or" and when they are seperate that means "and"
//
// Term is of the format:
//    ((-)text/RegExp)  ( '-' means negation )

chrishwiggins

ICERM Data Science: references and links

Talk delivered 2015-07-29 at ICERM workshop on "mathematics and data science"

PDF on slideshare of talk slides

context

• google search trends: data science

mathisonian

The final result: require() any module on npm in your browser console with browserify

This article is written to explain how the above gif works in the chrome (and other) browser consoles. A quick disclaimer: this whole thing is a huge hack, it shouldn't be used for anything seriously, and there are probably much better ways of accomplishing the same.

Update: There are much better ways of accomplishing the same, and the script has been updated to use a much simpler method pulling directly from browserify-cdn. See this thread for details: mathisonian/requirify#5

inspiration

vietjtnguyen

dypsilon

Frontend Development

Attention: the list was moved to

https://github.com/dypsilon/frontend-dev-bookmarks

This page is not maintained anymore, please update your bookmarks.

---

econchick

class Graph:
  def __init__(self):
    self.nodes = set()
    self.edges = defaultdict(list)
    self.distances = {}

  def add_node(self, value):
    self.nodes.add(value)

  def add_edge(self, from_node, to_node, distance):

simonsarris

Module 1, Introduction to Matter, Energy, and Direct Current, introduces the course with a short history
of electricity and electronics and proceeds into the characteristics of matter, energy, and direct current
(dc). It also describes some of the general safety precautions and first-aid procedures that should be
common knowledge for a person working in the field of electricity. Related safety hints are located
throughout the rest of the series, as well.

Module 2, Introduction to Alternating Current and Transformers, is an introduction to alternating current
(ac) and transformers, including basic ac theory and fundamentals of electromagnetism, inductance,
capacitance, impedance, and transformers.