I am working on OCR of a word document to recognize the content mentioned in the document. I observed that OCR generated python code is not showing the content available in the document in auto-generated python script in language studio. I just want to get the python script structure where I can see the tags which are focusing on the identifying the sentences without table content.
Is the approach I am looking for is right? Any flow that explains requirement is much appreciated.
This problem will be defined in Form Recognizer. In this factor, we cannot see anything related to the general text from an image or a text file like DOCX and PDF in python script which will be generated. The form recognition, related to the specific structure that is having pre-modelled scenarios of syntax. But the content which was mentioned as the input without any tabular form is not recognized in python. The content will be visible in JSON which is other than the tabular form.
Check out the thread for reference.
Azure Cognitive Form Recognizer to Extract Page Numbers using Python
Related
I want to use Document.ai to extract data from tables in my pdf.
I was following this code snippet https://cloud.google.com/document-ai/docs/handle-response#code_samples_2
But my table array is always empty. I tried to do it with pdf provided by google or with mine. But it doesn't work.
When I put this documents into drag'n'drop example it work flawlessly https://cloud.google.com/document-ai/docs/drag-and-drop
Is there some trick to make it work with python client?
Be sure that you're sending the document to a Form Parser processor, as that's the processor type that can extract tables.
If you're already using the form parser, it would be helpful to provide the document you're using and the sample output from Document AI.
Update: The Demo has been updated to show that Tables are extracted using the Form Parser.
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 am currently developing a proprietary PDF parser that can read multiple types of documents with various types of data. Before starting, I was thinking about if reading PowerPoint slides was possible. My employer uses presentation guidelines that requires imagery and background designs - is it possible to build a parser that can read the data from these PowerPoint PDFs without the slide decor getting in the way?
So the workflow would basically be this:
At the end of a project, the project report is delivered in the form of a presentation.
The presentation would be converted to PDF.
The PDF would be submitted to my application.
The application would read the slides and create a data-focused report for quick review.
The goal of the application is to cut down on the amount of reading that needs to be done by a significant amount as some of these presentation reports can be many pages long with not enough time in the day.
Parsing PDFs into structured data is always tricky, as the format is geared towards precise printing, rather than ease of editing or data extraction.
Basically, a PDF contains information like "there's a label with such text at such (x,y) position on a certain page", or things like that.
Basically, you will very likely need some heuristics in order to turn that into structured data.
It will basically be a form of scraping.
Search on your favorite search engine for PDF scraping, or something like that, and it would be a good start.
Also, you may want to look at those similar posts:
PDF Data and Table Scraping to Excel
How to extract table as text from the PDF using Python?
A PowerPoint PDF isn't a type of PDF.
There isn't going to be anything natively in the PDF that identifies elements on the page as being 'slide' graphics the originated from a PowerPoint file for example.
You could try building an algorithm that makes decision about content to drop from the created PDF but that would be tricky and seems like the wrong approach to me.
A better approach would be to "Export" the PPT to text first, e.g. in Microsoft PowerPoint Export it to a RTF file so you get all of the text out and use that directly or then convert that to PDF.
I'm making a web interface to autofill pdf forms with user data from a database. The admin needs to be able to upload a pdf (right now targeted at IRS pdf forms) and then associate the fields in the pdf with data fields in the database.
I need a way to help the admin associate the field names (stuff like "topmostSubform[0].Page2[0].p2-t66[0]") with the the data fields in the database. I'm looking for a way to modify the PDF programatically to in some way provide this information.
Basically I'm open to suggestions on how I might make the field names appear in an obvious manner on a modified version of the original pdf. The closest I've gotten is being able to insert Tooltips into the fields in the pdf by just editting the raw pdf line by line. However when editting the pdf in this manner the field names are gibberish, and so I can't just use them.
An optimal solution would be anything that could automatically parse a pdf and set each field's tooltip to be the fields name. Anything that can be run from the command line, or any python tool, or just a basic how to correctly parse a field's name from a raw pdf file would be amazing.
There may be an easier solution than this, but you could definitely get the job done with http://www.reportlab.com/software/opensource/rl-toolkit/'>ReportLab.
If you can save the current tax forms as an image, you could determine where each of the items need to be written and develop your code so that it automatically layers the appropriate values from the database on top of the image (the tax form, or whatever it might be).
Once you've determined 1) What fields need to be pulled from the database, and 2) where they 're supposed to go on within the form...
this is essentially what you'd be doing:
from reportlab.pdfgen import canvas
report_string_values = ['Alex',500,500],['Guido',400,400],
c = canvas.Canvas('hello.pdf')
c.drawImage(background_image,x_pos,y_pos) # x_pos and w_pos are # pixels from bl origin
for rsv in report_string_values:
c.drawString(rsv.x_pos,rsv.,rsv.text)
c.showPage()
c.save()
A postscript parser lives here: https://github.com/haxwithaxe/py-ps-parser
I've been interested in playing with it, but haven't yet.
This may be way off your intended track; but, it might be worth a think. I've been working on parsing scanned structured documents into Django model instances. Using tesseract and unpaper to do the pre-processing and OCR, I get over 99% accuracy. That lets me parse the OCR output text with the Levenshtein and re modules and do a simple new_instance = MyModel(parsed1, parsed2, ...).
It seems that you are trying to do something similar. Looking at the forms at http://www.irs.gov/formspubs/ They tend to have text labels left-adjacent to the fields. Using something like py-tesseract, you should be able to OCR the labels, overlay the OCR text over the form image and allow the user to select/edit the field labels.
There is a nice little tool, ocrfeeder https://live.gnome.org/OCRFeeder, that is written in python and should give you a basic idea of how the process works in a desktop app. Good luck.
Government Forms are usually not a standard PDF but a JavaScript driven XFA in a PDF wrapper, thus to enter the data programmatically you need a lookup table as the order is rarely the visual order.
Here the first field "single yes or no" "topmostSubform[0].Page1[0].c1_01[0]" is a checkbox designated well down the list of entries. of course none in this Form are "topmostSubform[0].Page2[0].p2-t66[0]" so you need totally different look-up table for each XFA. Otherwise follow the entries (luckilly there is some sence of sequencing in this form) so free format field "topmostSubform[0].Page1[0].f1_01[0]" is near "dependent:" etc.
There are XFA dedicated applications that can extract the positions of static fields, but if the fields are dynamically adapting then the page position would be a moving feast.
For XFA you need an intelligent dedicated Adobe listing (often xml / xlsx input output supplied on request from the relevant department), or build your own if Acrobat Pro does not block the attempt.
I may be interpreting the question wrong but I have a lot of experience in pdf generation with python/django because of the site that I worked on for 5 months. I would suggest using texlive. Basically what I did was built a generic tex template for a document and then used django templating to insert the fields. I rendered the template as if it were html using render_to_string and then generated it using the pdflatex command. I ran pdflatex using pythons subprocess module and a little extra. To do the generating I used this guys pdflatex module http://bit.ly/KaDMBp , with some modifications. All the things you need are in the core.py inside of the pdflatex directory.
Ex tex document (test.tex) )
\begin{document}
my name is {{input_name}} and i live in {{input_location}}.
\end{document}
Ex rendering template with django templating and render_to_string )
params={input_name:"andrew",input_location:"nyc"}
tex_doc = render_to_string('test.tex', params)
Ex generating as pdf)
pdflatex = PDFLatex(texfile=tex_path,outputdir=pdf_path)
pdflatex.transform()
Latex has a somewhat annoying, difficult learning curve but if you put in the time you can learn what you need to know in order to create these pdfs.
Hope this helps.
The SDAPS framework was designed for scenarios like this: It aids in batch-processing PDF-based forms, extract contents from designated fields and e.g. funnel those into a database for further processing.
How can you dynamically generate a .doc file using AJAX? Python? Adobe AIR? I'm thinking of a situation where an online program/desktop app takes in user feedback in article form (a la wiki) in Icelandic character encoding and then upon pressing a button releases a .doc file containing the user input for the webpage. Any solutions/suggestions would be much appreciated.
PS- I don't want to go the C#/Java way with this.
The problem with the *.doc MS word format is, that it isn't documented enough, therefor it can't have a very good support like, for example, PDF, which is a standard.
Except of the problems with generating the doc, you're users might have problems reading the doc files. For example users on linux machines.
You should consider producing RTF on the server. It is more standard, and thus more supported both for document generation, and for reading the document afterwards. Unless you need very specific features, it should suffice for most of documents types, and MS word opens it by default, just like it opens its own native format.
PyRTF is an project you can use for RTF generation with python.
It don't have to do much with ajax(in th sense that ajax is generally used for dynamic client side interactions)
You need a server side script which takes the input and converts it to doc.
You may use something like openoffice and python if it has some interface
see http://wiki.services.openoffice.org/wiki/Python
or on windows you can directly use Word COM objects to create doc using win32apis
but it is less probable, that a windows server serving python :)
I think better alternative is to generate PDF which would be nicer and easier.
Reportlab has a wonderful pdf generation library and it works like charm from python.
Once you have pdf you may use some pdf to doc converter, but I think PDF would be good enough.
Edit: Doc generation
On second thought if you are insisting on DOC you may have windows server in that case
you can use COM objets to generate DOC, xls or whatever see
http://win32com.goermezer.de/content/view/173/284/