Travis Taylor sivartravis

Copy-to-clipboard Bookmarklet

Create Bookmarklet (browser bookmark that executes Javsacript) to copy a given text to Clipboard.

This is the base javascript:

(function (text) {
  var node = document.createElement('textarea')
  var selection = document.getSelection()

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

This hit #rstats today:

Has anyone made a dumbbell dot plot in #rstats, or better yet exported to @plotlygraphs using the API? https://t.co/rWUSpH1rRl
— Ken Davis (@ken_mke) October 23, 2015

So, I figured it was worth a cpl mins to reproduce.

While the US gov did give the data behind the chart it was all the data and a pain to work with so I used WebPlotDigitizer to transcribe the points and then some data wrangling in R to clean it up and make it work well with ggplot2.

It is possible to make the top "dumbbell" legend in ggplot2 (but not by using a guide) and color the "All Metro A

	function onOpen() {
	var ui = DocumentApp.getUi();
	ui.createMenu('Convert to .RST')
	.addItem('Convert to .RST and email me the result', 'ConvertToRestructuredText')
	.addToUi();
	}

	// Adopted from https://github.com/mangini/gdocs2md by Renato Mangini
	// License: Apache License Version 2.0
	String.prototype.repeat = String.prototype.repeat \|\| function(num) {

	/**
	* Remove hentry from post_class
	* This is important to not get schema errors
	*/
	function visceral_remove_hentry_class( $classes ) {
	$classes = array_diff( $classes, array( 'hentry' ) );
	return $classes;
	}
	add_filter( 'post_class', 'visceral_remove_hentry_class' );

	// Display Tag code
	function ahmedkaludi_structured_data_keyword() {
	if ( is_single() ) {
	$tags = '"keywords" : [' . strip_tags(get_the_tag_list('"','", "','"')) . ']';
	} else { }
	echo $tags;
	}


	function ahmedkaludi_structured_data_output() {

	<?php
	// checks if there are any posts that match the query
	if ( have_posts() ) :

	// If there are posts matching the query then start the loop
	while ( have_posts() ) : the_post(); ?>

	<article id="post-<?php the_ID(); ?>" role="article" itemprop="hasPart" itemscope="" itemtype="http://schema.org/Article">
	<meta itemscope='itemscope' itemprop='mainEntityOfPage' itemType='https://schema.org/WebPage'/>

	function exportSpreadsheet() {

	//All requests must include id in the path and a format parameter
	//https://docs.google.com/spreadsheets/d/{SpreadsheetId}/export

	//FORMATS WITH NO ADDITIONAL OPTIONS
	//format=xlsx //excel
	//format=ods //Open Document Spreadsheet
	//format=zip //html zipped