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This python package is an OCR library which reads all text & tables from image & PDF files using an OCR engine & provides intelligent post-processing options to save OCR results in formats you want.

The package requiresPython 3 to run.

You can usepip to install:

pip install ocr-nanonets-wrapper

This software is perpetually free :)

You can get your free API key (with unlimited requests) by creating a free account onhttps://app.nanonets.com/#/keys.

fromnanonetsimportNANONETSOCRmodel=NANONETSOCR()model.set_token('REPLACE_API_KEY')

You can refer the code shared below ordirectly use code from here.

# InitialisefromnanonetsimportNANONETSOCRmodel=NANONETSOCR()# Authenticate# This software is perpetually free :)# You can get your free API key (with unlimited requests) by creating a free account on https://app.nanonets.com/#/keys?utm_source=wrapper.model.set_token('REPLACE_API_KEY')# PDF / Image to Raw OCR Engine Outputimportjsonpred_json=model.convert_to_prediction('INPUT_FILE')print(json.dumps(pred_json,indent=2))# PDF / Image to Stringstring=model.convert_to_string('INPUT_FILE')print(string)# PDF / Image to TXT Filemodel.convert_to_txt('INPUT_FILE',output_file_name='OUTPUTNAME.txt')# PDF / Image to Boxes# each element contains predicted word and bounding box information# bounding box information denotes the spatial position of each word in the fileboxes=model.convert_to_boxes('test.png')forboxinboxes:print(box)# PDF / Image to CSV# This method extracts tables from your file and prints them in a .csv file.# NOTE : This particular function is a trial offering 1000 pages of use.# To use this at scale, please create your own model at app.nanonets.com --> New Model --> Tables.model.convert_to_csv('INPUT_FILE',output_file_name='OUTPUTNAME.csv')# PDF / Image to Tables# This method extracts tables from your file and returns a json object.# NOTE : This particular function is a trial offering 1000 pages of use.# To use this at scale, please create your own model at app.nanonets.com --> New Model --> Tables.importjsontables_json=model.convert_to_tables('INPUT_FILE')print(json.dumps(tables_json,indent=2))# PDF / Image to Searchable PDFmodel.convert_to_searchable_pdf('INPUT_FILE',output_file_name='OUTPUTNAME.pdf')

To make getting started easier for you, there is a bunch of sample code along with sample input files.

• Clone or download the repo and open the /tests folder.
• all_tests.ipynb is a python notebook containing testing for all methods in the package.
• convert_to_{METHOD}.py files are python files corresponding to each method in the package individually.

Note

convert_to_string() and convert_to_txt() methods have two optional parameters -

1. formatting =

• `lines and spaces` : default, all formatting enabled
• `none` : space separated text with formatting removed
• `lines` : space separated text with lines separated with newline character
• `pages` : list of page wise space separated text

2. line_threshold =

• `low` : default
• `high` : You can addline_threshold='high' as a parameter while calling the method which in few cases can improve reading flowcharts and diagrams.

If extracting flat fields, tables and line items from PDFs and images is your use case, I will strongly advice you to create your own model by signing up onapp.nanonets.com and using our advanced API. This will improve functionalities, accuracy and response times significantly. Once you have created your account and model, you can useAPI documentation present here to extract flat fields, tables and line items from any PDF or image.

We help businesses automate Manual Data Entry Using AI and reduce turn around times & manual effort required. More than 1000 enterprises use Nanonets for Intelligent Document Processing. We have generated incredible ROIs for our clients.

We provide OCR and IDP solutions customised for various use cases - invoice automation, Receipt OCR, purchase order automation, accounts payable automation, ID Card OCR and many more.

• Visitnanonets.com for enterprise OCR and IDP solutions.
• Sign up onapp.nanonets.com/#/signup to start a free trial.

MIT

This software is perpetually free :)