PythonLabZalan

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

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