PythonLabZalan: Difference between revisions

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[[File:Screen Shot 2018-03-24 at 16.12.30.png|thumb|NLTK Analysis outcome]]
[[File:Screen Shot 2018-03-24 at 16.12.30.png|thumb|NLTK Analysis outcome]]


 
To be able to understand how NLTK works I did an intensive python beginners learning week from 26.02.–04.03.2018.
 
 
 
 
 
 
 
 
 


== Natural Language Tool Kit ==
== Natural Language Tool Kit ==

Revision as of 16:40, 24 March 2018

Terminal

Firstly I looked into basic command line functions File:Commands terminal.pdf and their operations for creating a solid base for Python3.

Optical character recognition + Tesseract

Secondarily I experimented in Terminal how to translate PDF or JPG to .txt files with tesseract and imagemagick (convert).

Optical character recognition

Input 1
Output 1

Tesseract (with languages you will be using)

  • Mac brew install tesseract --all-languages

imagemagick

  • Mac brew install imagemagick

How to use it?

tesseract - png - name of the txt file

tesseracttest SZAKACS$ tesseract namefile.png text2.txt

Getting 1 page from PDF file with PDFTK burst

pdftk yourfile.pdf burst

Or use imagemagick

convert -density 300 Typewriter\ Art\ -\ Riddell\ Alan.pdf Typewriter-%03d.tiff

Chose page you want to convert

Convert PDF to bit-map using imagemagick, with some options to optimize OCR

  • convert -density 300 page.pdf -depth 8 -strip -background white -alpha off ouput.tiff
  • -density 300 resolution 300DPI. Lower resolutions will create errors :)
  • -depth 8number of bits for color. 8bit depth == grey-scale
  • -strip -background white -alpha off removes alpha channel (opacity), and makes the background white
  • output.tiffin previous versions Tesseract only accepted images as tiffs, but currently more bitmap formats are accepted

Python3

Input 2
Output 2
NLTK Analysis outcome

To be able to understand how NLTK works I did an intensive python beginners learning week from 26.02.–04.03.2018.

Natural Language Tool Kit

For the NLTK text analysis I used one of pages of my reader. First NLTK Analysis in python3 (link to the script) to get different data from the textual input such as (see NLTK analysis outcome):

  • Amount of words
  • The number of lowercase letters
  • The number of uppercase letters
  • 10 most common characters
  • 10 most common words
  • more than 15 character long words of the text
  • Amount of Verbs
  • Amount of Nouns
  • Amount of Adverbs
  • Amount of Pronouns
  • Amount of Adjectives
  • Amount of lines

DrawBot

ACCP (Analogue Circular Communication Protocol