Skip to main content

A library for parsing various government documents as well as general PDFs

Project description

# Zen Document Parser

## Intro

zen_document_parser is a utility for extracting data from various official documents. It uses [PDFQuery](https://github.com/jcushman/pdfquery) behind the scenes.

Currently, there is out-of-the-box support for parsing **Indian Government ITR-V PDF documents.**

The library also supports parsing of arbitrary PDF documents by allowing you to specify a 'schema' for the document. The library allows for multiple 'variants' of a document. For example, The Indian ITR-V document has slightly different fields and layout depending on whether it was generated in 2013, 2014, 2015 etc.

Check out the examples below.


## Installation

Install using [pip](https://pip.pypa.io/en/stable/installing/) like so:

```bash

$ pip install zen_document_parser
```

## Usage

### ITR-V Docs

```python

from zen_document_parser.itr.itr import ITRVDocument

# You can pass in a path or a file-like object during instantiation.
doc = ITRVDocument('/path/to/itrv.pdf')

# Will load the file, auto-detect the variant and perform extraction of all
# fields and store results internally.
doc.extract()

# Extracted fields are available in the `data` property.
print(doc.data.company_name)
print(doc.data.gross_total_income)

```


### Configuring for custom PDF documents

You basically follow these steps:

- Define one or more 'schemas', ie. `DocVariant` subclasses, to go with each variant of the doc.
- In each of these variants, define a `check_for_match()` method that returns `True` if a file was successfully parsed.
- Make sure to define `test_fields` as an attribute on each class that is a list of all field names used inside `check_for_match()`. (This is required at present for optimization purposes, but will not be a requirement in an upcoming version.)
- Define a `Doc` subclass that represents your document. In the `variants` attribute, specify possible variants.


```python

from zen_document_parser.base import DocField, DocVariant, Document


class Variant1(DocVariant):

# The fields that are used inside `check_for_match()`. (for optimization)
test_fields = ['form_title']

form_title = DocField((30, 300, 500, 380))
name = DocField((100, 120, 400, 140.5))
address = DocField((150, 90, 650, 110))

def check_for_match(self):
if self.form_title == 'Application Form For 2014':
return True
return False


class Variant2(DocVariant):

test_fields = ['form_title']

form_title = DocField((30, 290, 500, 380))
name = DocField((70, 140, 350, 160))
address = DocField((150, 120, 650, 140))
pan_no = DocField((150, 80, 650, 100))

def check_for_match(self):
if self.form_title == 'Application Form For 2015-16':
return True
return False


class MyForm(Document):

variants = [Variant1, Variant2]


def main():
doc = MyForm('/path/to/form.pdf')
doc.extract()
print(doc.data.to_dict())
```


# TODO

- Hanle data-type specification
- Handle fields being mandatory/non-mandatory.
- Right now the user has to explicitly specify `test_fields` for optimization purposes. Find a way where this isn't needed.
- Automatically load them the first time they're referred to? `extract()` can still be there as a way to bulk-load all fields in one go.

Project details


Release history Release notifications

This version

0.11

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for zen_document_parser, version 0.11
Filename, size File type Python version Upload date Hashes
Filename, size zen_document_parser-0.11.tar.gz (7.9 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page