I need to extract data from tables in multiple PDF's using Python. I have tested both camelot and tabula however neither of them are able to accurately get the data. The tables have some merged cells, cells with mutiple lines of information etc. so both these libraries get confused. Is there a good way of approaching this issue?
There may be something wrong with the underlying structure of the table encoded in the PDF if that's the case.
You could use OCR, and do some string/regex manipulation to extract column data from each row. github.com/cseas/ocr-table seems to work. See the input.pdf and output.txt to see if it works with your situation.
Related
I have 3 tables (image pasted) all 3 table(have same columns) look same and i want data of address column (yellow colour) of 3 tables stored inside a variable.
There are different ways to handle extraction of tables from pdf. The final solution will depend primarily on individual pdf that you need to read. Some variables to think about when choosing the solution are:
is the pdf just an image saved as pdf (rastered image of a scanned
document)?
what is the quality of pdf?
is there any noise in the pdf
files (e.g. spots caused by a printer) you need to get rid of?
is the table in pdf skewed?
how many pages has a pdf?
how many pages a table spans across?
how many documents do you need to scan?
There are many solutions to extract tables from pdf ranging from table-specialized OCR services to python utility libraries to help you build your own extraction program.
An example of a powerful tool to convert data from tables from pdf to excel is Camelot, which you have included in your question's tags. It abstracts a lot of complexity involved in the task at hand. You just install it
and access it for example like that:
import camelot
file = 'https://www.w3.org/WAI/WCAG21/working-examples/pdf-table/table.pdf'
tables = camelot.read_pdf(file)
tables[0].to_excel('table.xlsx')
As I mentioned, the devil lies in the individual characteristics of a table and a pdf file.
I need to recognize written text in a table and parse it in json. I do it using python. I don't really understand how to extract photos of text from a table that is in pdf format. Because the usual table recognizer is not suitable, since written text is not recognized there. Accordingly, I need to somehow cut the cells from the table, how to do this?
if you want to extract tables and their cells you probably need a table extractor like this; 1
Then after extracting the table and its cells with their coordinates, you are allowed to pick those pixels. For example; img[x1:x2,y1:y2]
After obtaining the cell's pixels, you can use the Tesseract OCR engine to understand the texts written in image pixels.
These are the general steps that you need to follow, I can help you more if you precise your question more.
PDF format has not 'table' and 'cell'.
Covert PDF into PNG format or other raster format and use OCR as say BlackCode.
I have tried to extract table data from the image and insert to csv. I use by tesseract
can anyone tell me how to detect table data from the image
I have this image:
Check this open source library https://github.com/jsvine/pdfplumber. This has shown good promise in extracting table data. You will get the texts in the table as a list of lists which is very useful. Apart from that, you can get the coordinates of the cells also which gives provisions for any post-processing.
One drawback is that it works only for digital pdfs.
This question already has answers here:
How can I extract tables from PDF documents?
(4 answers)
Closed 14 days ago.
I am working on a pdf file. There is number of tables in that pdf.
According to the table names given in the pdf, I wanted to fetch the data from that table using python.
I have worked on html, xlm parsing but never with pdf.
Can anyone tell me how to fetch tables from pdf using python?
I think that you need a python parser library. The most famous is PDFMiner.
According to the documentation :
PDFMiner is a tool for extracting information from PDF documents. Unlike other PDF-related tools, it focuses entirely on getting and analyzing text data. PDFMiner allows one to obtain the exact location of text in a page, as well as other information such as fonts or lines. It includes a PDF converter that can transform PDF files into other text formats (such as HTML). It has an extensible PDF parser that can be used for other purposes than text analysis.
This is a very complex problem and not solvable in general.
The reason for this is simply that the format PDF is too flexible. Some PDFs are only bitmaps (you would have to do your own OCR then—obviously not our topic here), some are a bunch of letters literally spilled out over the pages; this means that by parsing the text information in the PDF you could get single characters placed on some coordinates. In some cases these come in an orderly fashion (line by line, from left to right), but in some cases you will get rather random-like distributions, most commonly with and stuff, but also special characters, characters in a different font etc. can come way out of line.
The only proper approach is to place all characters according to their coordinates on a page model and then use heuristics to find out what the lines are.
I propose to have a look at your PDFs and the tables therein you want to parse before starting. Maybe they are alike all the time and well-parsable.
Good luck!
I had a similar problem recently, and wrote a library to help solve it: pdfquery.
PDFQuery creates an element tree from the PDF (using pdfminer, with some extra sugar) and lets you fetch elements from the page using JQuery or XPath selectors, based mostly on the text contents or locations of the elements. So to parse a table, you would first find where it is in the document by searching for the label:
label = pdf.pq(':contains("Name of your table")')
left_corner = float(label.attr('x0'))
bottom_corner = float(label.attr('y0'))
Then you would keep searching for lines underneath the table, until the search didn't return results:
page = label.closest('LTPage')
while 1:
row = pdf.extract( [
('column_1', ':in_bbox("%s,%s,%s,%s")' % (left_corner+10, bottom_corner+40, left_corner+50, bottom_corner+20)),
('column_2', ':in_bbox("%s,%s,%s,%s")' % (left_corner+50, bottom_corner+40, left_corner+80, bottom_corner+20))
], page)
if not row['column_1'] or row['column_2']:
break
print "Got row:", matches
bottom_corner -= 20
This assumes that your rows are 20 pts high, the first one starts 20 pts below the label, the first column spans from 10 to 50 points from the left edge of the label, and the second column spans from 50 to 80 pts from the left edge of the label.
If you have blank lines or lines with varying heights, this is going to get more annoying. You may also need to use the merge_tags=None option to select individual characters rather than words, if the entries in the table are close enough to make the parser think it's just one line. But hopefully this gets you closer ...
You can use Camelot to extract tabular data from your PDF and export it to your favorite format. Currently; CSV, Excel, JSON and HTML are supported. You can check out the documentation at: http://camelot-py.readthedocs.io. It would be helpful if you could post a link to your PDF. Here's a generic code example:
>>> import camelot
>>> tables = camelot.read_pdf('file.pdf')
>>> type(tables[0].df)
<class 'pandas.core.frame.DataFrame'>
>>> tables[0].to_csv('file.csv')
Disclaimer: I'm the author of the library.
This question already has answers here:
How can I extract tables from PDF documents?
(4 answers)
Closed 14 days ago.
I am working on a pdf file. There is number of tables in that pdf.
According to the table names given in the pdf, I wanted to fetch the data from that table using python.
I have worked on html, xlm parsing but never with pdf.
Can anyone tell me how to fetch tables from pdf using python?
I think that you need a python parser library. The most famous is PDFMiner.
According to the documentation :
PDFMiner is a tool for extracting information from PDF documents. Unlike other PDF-related tools, it focuses entirely on getting and analyzing text data. PDFMiner allows one to obtain the exact location of text in a page, as well as other information such as fonts or lines. It includes a PDF converter that can transform PDF files into other text formats (such as HTML). It has an extensible PDF parser that can be used for other purposes than text analysis.
This is a very complex problem and not solvable in general.
The reason for this is simply that the format PDF is too flexible. Some PDFs are only bitmaps (you would have to do your own OCR then—obviously not our topic here), some are a bunch of letters literally spilled out over the pages; this means that by parsing the text information in the PDF you could get single characters placed on some coordinates. In some cases these come in an orderly fashion (line by line, from left to right), but in some cases you will get rather random-like distributions, most commonly with and stuff, but also special characters, characters in a different font etc. can come way out of line.
The only proper approach is to place all characters according to their coordinates on a page model and then use heuristics to find out what the lines are.
I propose to have a look at your PDFs and the tables therein you want to parse before starting. Maybe they are alike all the time and well-parsable.
Good luck!
I had a similar problem recently, and wrote a library to help solve it: pdfquery.
PDFQuery creates an element tree from the PDF (using pdfminer, with some extra sugar) and lets you fetch elements from the page using JQuery or XPath selectors, based mostly on the text contents or locations of the elements. So to parse a table, you would first find where it is in the document by searching for the label:
label = pdf.pq(':contains("Name of your table")')
left_corner = float(label.attr('x0'))
bottom_corner = float(label.attr('y0'))
Then you would keep searching for lines underneath the table, until the search didn't return results:
page = label.closest('LTPage')
while 1:
row = pdf.extract( [
('column_1', ':in_bbox("%s,%s,%s,%s")' % (left_corner+10, bottom_corner+40, left_corner+50, bottom_corner+20)),
('column_2', ':in_bbox("%s,%s,%s,%s")' % (left_corner+50, bottom_corner+40, left_corner+80, bottom_corner+20))
], page)
if not row['column_1'] or row['column_2']:
break
print "Got row:", matches
bottom_corner -= 20
This assumes that your rows are 20 pts high, the first one starts 20 pts below the label, the first column spans from 10 to 50 points from the left edge of the label, and the second column spans from 50 to 80 pts from the left edge of the label.
If you have blank lines or lines with varying heights, this is going to get more annoying. You may also need to use the merge_tags=None option to select individual characters rather than words, if the entries in the table are close enough to make the parser think it's just one line. But hopefully this gets you closer ...
You can use Camelot to extract tabular data from your PDF and export it to your favorite format. Currently; CSV, Excel, JSON and HTML are supported. You can check out the documentation at: http://camelot-py.readthedocs.io. It would be helpful if you could post a link to your PDF. Here's a generic code example:
>>> import camelot
>>> tables = camelot.read_pdf('file.pdf')
>>> type(tables[0].df)
<class 'pandas.core.frame.DataFrame'>
>>> tables[0].to_csv('file.csv')
Disclaimer: I'm the author of the library.