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doc-intel-1.0.1
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مشاهده بیشترتوضیحات
Your solution to cleansing PDF documents for preprocessing for NLP
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | doc-intel-1.0.1 |
| نام | doc-intel |
| نسخه کتابخانه | 1.0.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Vishak Arudhra |
| ایمیل نویسنده | vishakarudhra@gmail.com |
| آدرس صفحه اصلی | https://github.com/VishakArudhra/doc_intel |
| آدرس اینترنتی | https://pypi.org/project/doc-intel/ |
| مجوز | MIT |
# 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.
### Major Version Update 1.0.0 (10 / 19 / 2021) :
* A new module has been added to serve requirements of deletion and addition of words in the words text file.
* Usage demonstration can be found under the feature instruction section.
###### change 1.0.1 (10 / 21 / 2021) :
* fixed issue by which decimal numbers were spaced from the decimal point.
* closed the words text file in the main code block to prevent unclosed file issues.
## Feature instruction:
#### REMOVE THE HEADER AND FOOTER TERMS FORM 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:
###### Arguments :
* **serial numerical noise** [bool]: noise like `(text) + (random numbers, spaced or continous)` 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` --> ``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()
```
#### **ADD** AND **DELETE** WORDS FROM IN-BUILT DICTIONARY:
* PDF documents are not purposely written to suite document extraction and therefore, a lot of discontinued words in the documents end up broken with ordinary text extraction.
* doc-intel's in-built dictionary support identification of many 1000s of words which is now **editable**. Add and remove words as you require for your smooth text extraction.
* `do-cument` --> `document`
```
from doc_intel import manage_diction
word_list = " your list of words to either add or delete "
manage_diction.register_words(word_list).Add() #or
manage_diction.register_words(word_list).Delete()
```
###### **Authored & Maintained By** : Vishak Arudhra
نیازمندی
| مقدار | نام |
|---|---|
| - | pandas |
| - | Unidecode |
| - | unicodedata2 |
| - | clean-text |
| - | Levenshtein |
| - | PyMuPDF |
| - | regex |
نحوه نصب
نصب پکیج whl doc-intel-1.0.1:
pip install doc-intel-1.0.1.whl
نصب پکیج tar.gz doc-intel-1.0.1:
pip install doc-intel-1.0.1.tar.gz