how to generate a responsive PDF with Django?.
I want to generate a PDF with Django but i need that is responsive, that is to say, the text of the PDF has that adapted to don't allow space empty.
for example to a agreement this change in the text, then, i need to adapt the to space of paper leaf.
PDF is not built to be responsive, it is built to display the same no matter where it is viewed.
As #alxs pointed out in a comment, there are a few features that PDF viewing applications have added to simulate PDFs being responsive. Acrobat's Reflow feature is the best example of this that I am aware of and even it struggles with most PDFs that users come across in the wild.
One of the components (if not the only one) that matters, is that in order for a PDF to be useful in Acrobat's Reflow mode is to make sure that the PDFs you are creating contain structure information, this would be a Tagged PDF. Tagged PDF contains content that has been marked, similar to HTML tags, where text that makes up a paragraph is tagged in the PDF as being a paragraph. A number of PDF tools (creation or viewing) do not interpret the structure of a PDF though.
Related
Can an unstructured PDF be tagged using any tools/libraries?
Only source of tagging a PDF was using Adobe Acrobat or Auto-Tag APIs (Not something which I am looking forward to + not so great results imo)
I know the bounding boxes and semantics of the elements (i.e paragraph, lists, headings, tables)
So, is there a way to manipulate PDF trees/objects? preferably in Python or JavaScript.
Any thoughts on the topic is appreciated!!
PDF spec Talks about "StructTreeRoot" for Tagged PDFs. Going deep inside for making these objects would be
nerve-racking, so is there any high-level library to manipulate objects?
Tagging a PDF with all that entails needs to be done by the PDF writer so here is this page as Tagged by Chromium/Foxit/Skia in MS Edge.
Consider how impossible this may be to do retrospectively word by word or even sentence or paragraph at a time, as PDF does not inherently have such constructions.
Things like H1 are discarded by the paper printout generator as unrequired superfluous bloat for a printer.
OK the prime reason for tagging is the human challenged reader, so with a tagged PDF lets see how it fares. Here we are only dealing with one simple page without images or tables (the two most common reasons for checking tags)
So programmatically how will an iterative application driven by Python resolve the residual requirements which are missing.
Language, as a Human I know the language is English (that should have been obvious to a browser that speaks aloud)
The Title is missing but again that should be obvious is "TAGGING PDFS" suitable as a working title for approval by another person?
Lets temporarily ignore the major errors that tagging and order of tabs is wrong. A human with eyes and brain to analyse why, can fix those, as they progress through all the pages human aspects, so can the "Human" read / navigate logically? will itself resolve the tags order, and at the same time, check if the page is visually suitable contrast for visually challenged.
So the tagging of a PDF is best done at the time a human completes their retrospective use of the page, and that is best done using "Pre-flight" "Post-flight" GUI applications, such as Acrobat.
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 am working on a piece of software that analyses PDF files and generates HTML based on them. There are a number of things out there that already do this so I know it is possible, I have to write my own for business reasons.
I have managed to get all the text information, positions, fonts out of the PDF but I am struggling to read out the colour of the text. I am currently using PDFMiner to analyse the PDF but am beginning to think I will need to write my own PDFReader, even so, I can't figure out where in the document the Colour information for text is even kept! I have even read the PDF spec but cannot find the information I need.
I have scoured google, with no joy.
Thanks in advance!
The colour for text and other filled graphics is set using one of the g, rg or k operators in the content stream object in the PDF file, as described in section 4.5.7 Color Operators in the PDF reference manual.
The example G.3 Simple Graphics Example in the reference manual shows these operators being used to stroke and fill some shapes (but not text).
http://www.adobe.com/devnet/pdf/pdf_reference.html
When parsing a PDF file yourself you start by reading the trailer
at the end of the file which contains the file offset of the
cross reference table. This table contains the file offset of
each object in the PDF file. The objects are in a tree structure with references
to other objects. One of the objects will be
the content stream. This is described in sections 3.4 File Structure
and 3.6 Document Structure in the PDF reference manual.
It is possible to parse the PDF file yourself but this is
quite a lot of work. The content
stream may be compressed, contain references to other objects,
contain comments, etc. and you must handle all of these cases.
The PDFMiner software is already reading the content stream. Perhaps it
would be easier to extend PDFMiner to report the colour
of the text too?
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.