doc-intel 0.0.10

Your solution to cleansing PDF documents for preprocessing for NLP

doc_intel

pip install doc-intel

This package is subject to several potential fixes and until then any benefits derived by using this is very much intended.

doc_intel is your solution to a largely cleansed and intact text extract from a PDF.

change 0.0.1 (8 / 23 / 2021) :

• Line breaks lead to breakage of full words into smaller potentially non dictionary words and changes have been made to fix that.
• A dictionary has been used to identify how to reconstruct the broken words.

change 0.0.2 (8 / 28 / 2021) :

• Updated dictionary, maybe inverse document frequency will later be used instead to fix the line breaks.
• Inverse document frequency has been used to precisely split and reconstruct stitched or meaninglessly spaced words.

change 0.0.3 (9 / 10 / 2021) :

• added and removed words in word.txt, fixed dot spacing and conserved number word detachment post processing"

change 0.0.4 (9 / 14 / 2021) :

• added and removed words in word.txt, maintained the positions of apostrophes.

Feature instruction:

• remove header and footer terms in your document :

from doc_intel import text_laundry

file_path = / your_path/ your_file.pdf

texts = text_laundry.head_foot(file_path).remove()

• scrub off textual noise from your texts:

Arguements :

• serial numerical noise [bool]: noise like some textual piece but then 101 234 384 927 so all these numbers will be removed if needed will be removed.

• sentences or words interrupted with special characters [bool] : All cohesive words and sentences interruptions like co -hesive or sol **utions should be removed.

• lower case [bool] : toggle between bool values for lower or upper casing.

• s u b s t r i n g s w h i c h a r e a t t a c h e d w i l l b e s e p a r a t e d will be separated to substrings which are attached will be separated based on the fequency of the constituent words in the document.

text_object = text_laundry.load_text([input str], remove_serial, sents_or_word_breaks, lower)
cleaned_text = text_object.launder()

Authored & Maintained By : Vishak Arudhra