pdfinsight 0.0.8

Text Mining & Classification Toolkit

PDFInsight

Text Mining & Classification Toolkit

Extract and categorise text-based PDFs into the following categories

• table of contents
• header
• heading
• tables
• content
• footnote
• footer
• page number
• unsure (text that cannot be categorised)

Prepare the extracted content into a document store ready for ingestion by a sentence transformers model

Installation

pip install pdfinsight

Example

from pdfinsight import pdf_extractor, remove_toc, pivot_df_by_heading, df2docstore
df = pdf_extractor("sample.pdf")
df

	file	page	block	refined_block	block_ymin_diff	block_is_list	...	font_characteristics	font	font_color	text	image	cat
0	tests/sample.pdf	1	2	1	NaN	False	...	0	Calibri	0	THIS IS A		toc
1	tests/sample.pdf	1	3	2	95.0	False	...	0	Calibri	0	SAMPLE PDF		toc
2	tests/sample.pdf	2	1	3	-373.0	False	...	0	Calibri	0	Sample PDF		toc
3	tests/sample.pdf	2	6	5	-707.0	False	...	16	Calibri-Bold	0	TITLE		toc
4	tests/sample.pdf	2	7	6	30.0	False	...	0	Calibri	0	Lorem ipsum dolor sit amet, consectetur adipis...		toc
...	...	...	...	...	...	...	...	...	...	...	...	...	...
76	tests/sample.pdf	3	15	20	-15.0	False	...	0	Calibri	0	3956		table
77	tests/sample.pdf	3	13	20	59.0	False	...	0	Calibri	0	euismod sit amet tortor.		table
78	tests/sample.pdf	3	14	20	-15.0	False	...	0	Calibri	0	rhoncus semper.		table
79	tests/sample.pdf	3	3	15	730.0	False	...	0	Calibri	4485572	THIS IS A FOOTER		footer
80	tests/sample.pdf	3	4	15	14.0	False	...	0	Calibri	4485572	Page 3 of 3		footer

# remove rows where cat column is marked as 'toc' 
df = remove_toc(df)

# pivot the dataframe such that for each row's text, it will
# 1) merge with previous row if they are of the same category
# 2) it will iteratively search up the rows to search for the 
#    relevant headings for the row's text
pivot_df = pivot_df_by_heading(df)
pivot_df

	file	heading1	heading2	content
0	tests/sample.pdf	TITLE	Maecenas eu dapibus diam.	a) Suspendisse id sem sed lacus luctus digniss...
1	tests/sample.pdf	TITLE	Proin at lorem eu	Proin at lorem eu urna volutpat dignissim vel ...

# set the links to be the same as the filename
link_dict = dict(zip(transformed_df.file.unique(), transformed_df.file.unique()))

# convert the pivot_df into a dictionary format suitable for ingestion by a sentence transformers model
docStore = df2docstore(pivot_df, chunk_size = 100, link_dict = link_dict)
docStore

[{'content': 'TITLE\nMaecenas eu dapibus diam.\na) Suspendisse id sem sed lacus luctus dignissim ac eu mi. Praesent eu nisl enim. Etiam ac libero sapien. Mauris at eros neque. Vestibulum lectus ligula, tempor accumsan nunc ut, sodales rhoncus purus. Duis vel tristique ipsum. Ut nec nulla et turpis finibus pulvinar. Aenean eleifend malesuada sapien vel malesuada. Proin viverra nisi non tellus congue auctor. Nam euismod gravida dui at aliquet. Praesent vitae facilisis libero. Donec vestibulum sodales augue, et commodo mauris. Ut sodales interdum ex, quis feugiat nisi fringilla sit amet. Fusce eget neque ac est ullamcorper tristique at nec leo.\nb) Donec sem enim, fermentum sit amet tincidunt sed, semper sit amet odio. Quisque vitae odio turpis. Aliquam erat volutpat. Suspendisse potenti. Praesent sit amet viverra enim, porta pellentesque turpis. Ut posuere lacus pharetra sapien maximus, sed feugiat mi eleifend. Duis congue eros in blandit varius. Maecenas efficitur, urna sed commodo pulvinar, mi nisi consectetur augue, vel sagittis tortor risus nec tellus. Nam sollicitudin lacus eu enim fringilla, vel dapibus quam mattis. Nullam ac leo et 1 mauris pharetra dictum ac quis tortor . Aenean nulla mi, semper ac ultricies sed, placerat et erat. Maecenas ullamcorper, orci eget pulvinar fermentum, magna tortor laoreet magna, eget lacinia quam nulla vitae nulla. Quisque augue lacus, ullamcorper in nunc sed, sodales accumsan dui.', 'source': 'tests/sample.pdf', 'update': ''}, {'content': 'TITLE\nProin at lorem eu\nProin at lorem eu urna volutpat dignissim vel nec erat. Mauris ac dui vel felis rutrum malesuada eget quis ante. Phasellus elementum porta lorem, eu sagittis tortor congue sed. Vivamus nec diam sagittis, sagittis erat nec, lacinia erat. Maecenas at leo metus. Vestibulum sit amet diam ut leo accumsan pharetra. Proin tincidunt vestibulum tincidunt. Pellentesque purus nibh, fermentum sit amet dui at, maximus porttitor sapien.\nColumn 1 Column 2 Column 3 Praesent varius consequat id ultricies diam aliquam 456 justo, volutpat\nVestibulum ante ipsum\net posuere elit elit sed orc 567\nprimis in faucibus orci luctus et ultrices posuere cubilia curae;\ncongue nec molestie et, Nullam posuere nibh ut nisi 3956 euismod sit amet tortor. rhoncus semper.', 'source': 'tests/sample.pdf', 'update': ''}]

References

https://github.com/pymupdf/PyMuPDF