gr8Adakron

Insert and Select

• Cmd+D - Select a word
• Cmd+Shift+Enter - Insert a line before current line
• Cmd+Enter - Insert a line after current line
• Cmd+L - Select current line

Delete

• Cmd+Shift+K - Delete a line
• Cmd+K+K - Delete from cursor to end of line.
• Cmd+KBackspace - Delete from Cursor to start of line

gr8Adakron

#author: gr8_adakron.
#python: 3.6 (necessary for fstrings)

from subprocess import PIPE, Popen
from multiprocessing import Pool

import multiprocessing as mp
import pandas as pd

import random

gr8Adakron

#Author : gr8_Adakron.

use Term::ANSIColor;
use Time::HiRes qw(time);
use strict;
use warnings;
use Carp;
use POSIX ":sys_wait_h";
use Data::Dumper;

gr8Adakron

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

gr8Adakron

#!/usr/bin/perl
#Author: gr8_Adakron.
#--------------------- Perl Packages --------------------
use strict;
use warnings;
use JSON;
use Text::CSV;

#-------------------- Globaling Variables --------------------
my $flag_header = 1;

gr8Adakron

import requests
from requests.auth import HTTPBasicAuth
#--------------------------------------------------------------------------#
api_key = 'Put_Your_API_KEY' #your Api_key from developer.zamzar.com (Create Signup id and get api_key)
source_file = "tmp/armash.pdf" #source_file_path
target_file = "results/armash.txt" #target_file_path_and_name
target_format = "txt"  #targeted Format.
#-------------------------------------------------------------------------#

gr8Adakron

import pandas as pd
List1=[10,11,12,15,16,18,19]
List2=[11,15,16,19,13]
List3=[11,12,15,19]

d = {'List1' : pd.Series(List1),'List2' : pd.Series(List2),'List3': pd.Series(List3)}

df = pd.DataFrame(d)

print(df)