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

fay-jai

import React from "react";
import { render } from "react-dom";

const ParentComponent = React.createClass({
  getDefaultProps: function() {
    console.log("ParentComponent - getDefaultProps");
  },
  getInitialState: function() {
    console.log("ParentComponent - getInitialState");
    return { text: "" };

primaryobjects

library(caret)
library(tm)

# Training data.
data <- c('Cats like to chase mice.', 'Dogs like to eat big bones.')
corpus <- VCorpus(VectorSource(data))

# Create a document term matrix.
tdm <- DocumentTermMatrix(corpus, list(removePunctuation = TRUE, stopwords = TRUE, stemming = TRUE, removeNumbers = TRUE))

kane-thornwyrd

var target = {
  foo: {
    bar: {
      baz: [
        'madness'
      ]
    }
  }
};

mbejda

thiloplanz

Zero knowledge databases

The idea

The idea is to provide a database as a service to end users in such a way that no one except the user herself can access the data, not even the hosting provider or the database administrator.

Advantages

• A privacy- and/or security-conscious user will have more trust in such a setup.
• The service provider cannot be coerced to release the data they were trusted with, and he cannot be held responsible for the content he is storing.

glamp

import pandas as pd
# http://blog.yhathq.com/static/misc/data/WineKMC.xlsx
df_offers = pd.read_excel("./WineKMC.xlsx", sheetname=0)
df_offers.columns = ["offer_id", "campaign", "varietal", "min_qty", "discount", "origin", "past_peak"]
df_offers.head()

df_transactions = pd.read_excel("./WineKMC.xlsx", sheetname=1)
df_transactions.columns = ["customer_name", "offer_id"]
df_transactions['n'] = 1
df_transactions.head()

PurpleBooth

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

alediaferia

/*
Copyright (c) 2015-2020 Alessandro Diaferia

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

mininao

	for (int i = 0; i < length; ++i)
	{
		struct LED current_led = leds[i];
		
		if(current_led.color.red != 0) {
		  on(current_led.pos, true, false,false);
		  delayMicroseconds(current_led.color.red);
		  off(current_led.pos);
                }