I am trying to extract a table from a PDF document (example). It's not a scan/an image, so please focus on non-OCR solutions. OCR table extraction is here.
I tried the route of pdf -> html -> extract table. The pdf that I mentioned above when converted to html produces garbage, maybe because of the font, the document is not in English.
Extracting the pdf using x and y coordinate is not an option as this solution needs to work for future pdf from the url mention above which will have the table but not always in the same position.
The PDF does not contain explicit table data. It only contains lines and character glyphs which we tend to interpret as tables. Thus your task involves putting our human table recognition capabilities into code which is quite a task.
Generally speaking, if you are sure enough future PDFs will be generated by the same software in a very similar manner, it might be worth the time to investigate the file for some easy to follow hints to recognize the contents of individual fields.
Your specific document, though, has an additional shortcoming: It does not contain the required information for direct text extraction! You can try copying & pasting from Adobe Reader and you'll get (at least I do) semi-random characters from the WinAnsi range.
This is due to the fact that all fonts in the document claim that they use WinAnsiEncoding even though the characters referenced this way definitively are not from the WinAnsi character selection.
Thus reliable text extraction from your document without OCR is impossible after all!
(Trying copy&paste from Adobe Reader generally is a good first test whether text extraction is feasible at all; the text extraction methods of the Reader have been developed for many many years and, therefore, have become quite good. If you cannot extract anything sensible with Acrobat Reader, text extraction will be a very difficult task indeed.)
You could use Tabula:
http://tabula.nerdpower.org
It's free and kinda easy to use
One option is to use pdf-table-extract: https://github.com/ashima/pdf-table-extract.
Extracting tables from PDF documents is extremely hard as PDF does not contain a semantic layer.
Camelot
You can try camelot, maybe even in combination with its web interface excalibur:
>>> import camelot
>>> tables = camelot.read_pdf('foo.pdf')
>>> tables
<TableList n=1>
>>> tables.export('foo.csv', f='csv', compress=True) # json, excel, html, markdown, sqlite
>>> tables[0]
<Table shape=(7, 7)>
>>> tables[0].parsing_report
{
'accuracy': 99.02,
'whitespace': 12.24,
'order': 1,
'page': 1
}
>>> tables[0].to_csv('foo.csv') # to_json, to_excel, to_html, to_markdown, to_sqlite
>>> tables[0].df # get a pandas DataFrame!
See also python-camelot
Tabula
tabula can be installed via
pip install tabula-py
But it requires Java, as tabula-py is only a wrapper for the Java project.
It's used like this:
import tabula
# Read pdf into list of DataFrame
dfs = tabula.read_pdf("test.pdf", pages='all')
See also:
Reading a specific table with tabula
tabula
AWS Textract
I haven't tried it recently, but AWS Textract claims:
Amazon Textract can extract tables in a document, and extract cells, merged cells, and column headers within a table.
PdfPlumber
pdfplubmer table extraction methods:
import pdfplumber
pdf = pdfplumber.open("example.pdf")
page = pdf.pages[0]
page.extract_table()
See also
Tabula vs Camelot
Related
I am trying to extract a table from a PDF document (example). It's not a scan/an image, so please focus on non-OCR solutions. OCR table extraction is here.
I tried the route of pdf -> html -> extract table. The pdf that I mentioned above when converted to html produces garbage, maybe because of the font, the document is not in English.
Extracting the pdf using x and y coordinate is not an option as this solution needs to work for future pdf from the url mention above which will have the table but not always in the same position.
The PDF does not contain explicit table data. It only contains lines and character glyphs which we tend to interpret as tables. Thus your task involves putting our human table recognition capabilities into code which is quite a task.
Generally speaking, if you are sure enough future PDFs will be generated by the same software in a very similar manner, it might be worth the time to investigate the file for some easy to follow hints to recognize the contents of individual fields.
Your specific document, though, has an additional shortcoming: It does not contain the required information for direct text extraction! You can try copying & pasting from Adobe Reader and you'll get (at least I do) semi-random characters from the WinAnsi range.
This is due to the fact that all fonts in the document claim that they use WinAnsiEncoding even though the characters referenced this way definitively are not from the WinAnsi character selection.
Thus reliable text extraction from your document without OCR is impossible after all!
(Trying copy&paste from Adobe Reader generally is a good first test whether text extraction is feasible at all; the text extraction methods of the Reader have been developed for many many years and, therefore, have become quite good. If you cannot extract anything sensible with Acrobat Reader, text extraction will be a very difficult task indeed.)
You could use Tabula:
http://tabula.nerdpower.org
It's free and kinda easy to use
One option is to use pdf-table-extract: https://github.com/ashima/pdf-table-extract.
Extracting tables from PDF documents is extremely hard as PDF does not contain a semantic layer.
Camelot
You can try camelot, maybe even in combination with its web interface excalibur:
>>> import camelot
>>> tables = camelot.read_pdf('foo.pdf')
>>> tables
<TableList n=1>
>>> tables.export('foo.csv', f='csv', compress=True) # json, excel, html, markdown, sqlite
>>> tables[0]
<Table shape=(7, 7)>
>>> tables[0].parsing_report
{
'accuracy': 99.02,
'whitespace': 12.24,
'order': 1,
'page': 1
}
>>> tables[0].to_csv('foo.csv') # to_json, to_excel, to_html, to_markdown, to_sqlite
>>> tables[0].df # get a pandas DataFrame!
See also python-camelot
Tabula
tabula can be installed via
pip install tabula-py
But it requires Java, as tabula-py is only a wrapper for the Java project.
It's used like this:
import tabula
# Read pdf into list of DataFrame
dfs = tabula.read_pdf("test.pdf", pages='all')
See also:
Reading a specific table with tabula
tabula
AWS Textract
I haven't tried it recently, but AWS Textract claims:
Amazon Textract can extract tables in a document, and extract cells, merged cells, and column headers within a table.
PdfPlumber
pdfplubmer table extraction methods:
import pdfplumber
pdf = pdfplumber.open("example.pdf")
page = pdf.pages[0]
page.extract_table()
See also
Tabula vs Camelot
Can any one help me on how to convert pdf file to xml file using python code? My pdf contains:
Unstructured data
It has images
Mathematical equations
Chemical Equations
Table Data
Logo's tag's etc.
I tried using PDFMiner, but my pdf data was not converted into .xml/json file format. Are there any libraries other than PDFMiner? PyPDF2, Tabula-py, PDFQuery, comelot, PyMuPDF, pdf to dox, pandas- these other libraries/utilities all not suitable for my requirement.
Please advise me on any other options. Thank you.
The first thing I would recommend you trying is GROBID (see here for the full documentation). You can play with an online demo here to see if fits your needs (select TEI -> Process Fulltext Document, and upload a PDF). You can also check out this from the Allen Institute (it is based on GROBID and has a handy function for converting TEI.XML to JSON).
The other package which--obviously--does a good job is the Adobe PDF Extract API (see here). It's of course a paid service but when you register for an account you get 1.000 document transactions for free. It's easy to implement in Python, well documented, and a good way for experimenting and getting a feel for the difficulties of reliable data extraction from PDF.
I worked with both options to extract text, figures, tables etc. from scientific papers. Both yielded good results. The main problem with out-of-the-box solutions is that, when you work with complex formats (or badly formatted docs), erroneously identified document elements are quite common (for example a footnote or a header gets merged with the main text). Both options are based on machine learning models and, at least for GROBID, it is possible to retrain these models for your specific task (I haven't tried this so far, so I don't know how worthwhile it is).
However, if your target PDFs are all of the same (simple) format (or if you can control their format) you should be fine with either option.
I need to extract tables from pdf, these tables can be of any type, multiple headers, vertical headers, horizontal header etc.
I have implemented the basic use cases for both and found tabula doing a bit better than camelot still not able to detect all tables perfectly, and I am not sure whether it will work for all kinds or not.
So seeking suggestions from experts who have implemented similar use case.
Example PDFs: PDF1 PDF2 PDF3
Tabula Implementation:
import tabula
tab = tabula.read_pdf('pdfs/PDF1.pdf', pages='all')
for t in tab:
print(t, "\n=========================\n")
Camelot Implementation:
import camelot
tables = camelot.read_pdf('pdfs/PDF1.pdf', pages='all', split_text=True)
tables
for tabs in tables:
print(tabs.df, "\n=================================\n")
Please read this: https://camelot-py.readthedocs.io/en/master/#why-camelot
The main advantage of Camelot is that this library is rich in parameters, through which you can improve the extraction.
Obviously, the application of these parameters requires some study and various attempts.
Here you can find comparision of Camelot with other PDF Table Extraction libraries.
I think Camelot better extracts data in a clean format and not jumbled up ( i.e. data retains the information and row contents are not affected).
So, The quality of data extracted is better in case of difference in the number of lines per cells .
->Tabula requires a Java Runtime Environment
There are open (Tabula, pdf-table-extract) source (smallpdf, PDFTables) tools that are widely used to extract tables from PDF files. They either give a nice output or fail miserably. There is no in between. This is not helpful since everything in the real world, including PDF table extraction, is fuzzy. This leads to the creation of ad-hoc table extraction scripts for each type of PDF table.
Camelot was created to offer users complete control over table extraction. If you can’t get your desired output with the default settings, you can tweak them and get the job done!
I am trying to build a small program in which I open a docx document and replace characters by others, to do some old school caesar-style encrypting, after checking the documentation: [ https://python-docx.readthedocs.io ] I am afraid I can't find the object methods and attributes, the documentation just kind-of explains how to do certain stuff like creating paragraphs and sections but I can't find anything on retrieving document data and parsing. I would like to find a list of the objects in the document so I can parse through them.
I would like to do something like this:
from docx import Document
document = Document('essay.docx')
paragraph = []
for i in document:
paragraph.append(i)
for i in paragraph:
for y in i:
y.replace("a", "y")
...
Can python-docx do something like this? If so where would I find the documentation that could show me how to do it?
If maybe I am using the incorrect library I would also appreciate it if you could point it out.
The API documentation is indexed (i.e. its table of contents appears) on the page you link to and describes all the objects and methods. https://python-docx.readthedocs.io/en/latest/#api-documentation
I think I found something useful in case future readers might be interested. The problem with python-docx is I could get paragraphs individually and it would take a lot of time. I don't even know if titles, footers and headers count as paragraphs.
But there is a library called textract that can read docx and other files, it integrates with python-docx, or at least that's what the short documentation says. But what I can do, is save my docx file to PDF and use:
text = textract.process(
'path/to/norwegian.pdf',
method='pdftofile',
language='nor',
)
This allows you to get all the text as a string and save it preserving the layout of the pdf. Haven't tested it yet, will edit this post if it doesn't work as intended.
http://textract.readthedocs.io/en/latest/python_package.html#python-package
I am trying to extract a table from a PDF document (example). It's not a scan/an image, so please focus on non-OCR solutions. OCR table extraction is here.
I tried the route of pdf -> html -> extract table. The pdf that I mentioned above when converted to html produces garbage, maybe because of the font, the document is not in English.
Extracting the pdf using x and y coordinate is not an option as this solution needs to work for future pdf from the url mention above which will have the table but not always in the same position.
The PDF does not contain explicit table data. It only contains lines and character glyphs which we tend to interpret as tables. Thus your task involves putting our human table recognition capabilities into code which is quite a task.
Generally speaking, if you are sure enough future PDFs will be generated by the same software in a very similar manner, it might be worth the time to investigate the file for some easy to follow hints to recognize the contents of individual fields.
Your specific document, though, has an additional shortcoming: It does not contain the required information for direct text extraction! You can try copying & pasting from Adobe Reader and you'll get (at least I do) semi-random characters from the WinAnsi range.
This is due to the fact that all fonts in the document claim that they use WinAnsiEncoding even though the characters referenced this way definitively are not from the WinAnsi character selection.
Thus reliable text extraction from your document without OCR is impossible after all!
(Trying copy&paste from Adobe Reader generally is a good first test whether text extraction is feasible at all; the text extraction methods of the Reader have been developed for many many years and, therefore, have become quite good. If you cannot extract anything sensible with Acrobat Reader, text extraction will be a very difficult task indeed.)
You could use Tabula:
http://tabula.nerdpower.org
It's free and kinda easy to use
One option is to use pdf-table-extract: https://github.com/ashima/pdf-table-extract.
Extracting tables from PDF documents is extremely hard as PDF does not contain a semantic layer.
Camelot
You can try camelot, maybe even in combination with its web interface excalibur:
>>> import camelot
>>> tables = camelot.read_pdf('foo.pdf')
>>> tables
<TableList n=1>
>>> tables.export('foo.csv', f='csv', compress=True) # json, excel, html, markdown, sqlite
>>> tables[0]
<Table shape=(7, 7)>
>>> tables[0].parsing_report
{
'accuracy': 99.02,
'whitespace': 12.24,
'order': 1,
'page': 1
}
>>> tables[0].to_csv('foo.csv') # to_json, to_excel, to_html, to_markdown, to_sqlite
>>> tables[0].df # get a pandas DataFrame!
See also python-camelot
Tabula
tabula can be installed via
pip install tabula-py
But it requires Java, as tabula-py is only a wrapper for the Java project.
It's used like this:
import tabula
# Read pdf into list of DataFrame
dfs = tabula.read_pdf("test.pdf", pages='all')
See also:
Reading a specific table with tabula
tabula
AWS Textract
I haven't tried it recently, but AWS Textract claims:
Amazon Textract can extract tables in a document, and extract cells, merged cells, and column headers within a table.
PdfPlumber
pdfplubmer table extraction methods:
import pdfplumber
pdf = pdfplumber.open("example.pdf")
page = pdf.pages[0]
page.extract_table()
See also
Tabula vs Camelot