Document Digitization Using OCR and Deep Learning

With the advent of OCR techniques, much time has been saved by automatically extracting the text out of a digital image of any invoice or a document. Currently, this is where most organisations that use OCR for any form of automation are.

Digital copies of invoices or documents are obtained by scanning or taking pictures. The text is extracted from these documents using OCR. Relevant data is extracted and the irrelevant data is discarded from the OCR results. The data is entered into a template based data entry software. The data entered is put through manual review to correct errors.

This templates used are unique to each use case, organisation and mostly for each different kind of document. While the OCR process helps in digitization, it doesn't solve many tedious parts due to the unstructured results of OCR.

Document digitization using Nanonets OCR

OCR, deep learning and digitization

By using OCR and deep learning, we have enabled machines to perform as well and in some cases even better than humans.

  • Digitizing invoices involves several labour intensive human moderated steps :
  • Digital images of invoices taken and uploaded by the user.
  • Image verified to be fit for further processing - good resolution, all data visible in the image, dates verified, etc.
  • Images checked for fraud.
  • Text in these images extracted and put in the right format.
  • Text data entered into tables, spreadsheets, databases, balance sheets, etc.

Deep learning approaches have seen advancement in the particular problem of reading the text and extracting structured and unstructured information from images. By merging existing deep learning methods with optical character recognition technology, companies and individuals have been able to automate the process of digitizing documents and enabled easier manual data entry procedures, better logging and storage, lower errors and better response times.

How to implement digitization in your organisation?

Lets try to understand with an example. Say we are building a vendor repayment system. This requires us to include several steps. Finding a workflow for your organizational needs is not the same as building a machine learning model that will give you good accuracy.

What you need is models that can deliver at least human-level accuracy, handle all sorts of data, accommodate error handling, increase the convenience of human supervision, provide transparency in the data processing steps, check for fraud, allow post-processing OCR results to put them in a structure, allow easy storing and databasing of this data and allow automating notification procedures depending on the results.

This is, as you might have guessed, a long and difficult procedure, often with not so straight forward solutions.

Digitization with Nanonets

With Nanonets you do not have to worry about finding machine learning talent, building models, understanding cloud infrastructure or deployment techniques. All you need is a business problem that you need solutions for.

Nanonets provides -

  1. Easy to use web-based GUI - you can build models on your own data, train them and get predictions in a convenient JSON format, without writing a single line of code.
  2. Cloud-hosted models - The models hostel on cloud can be accessed anytime from anywhere. All you need is an internet connection.
  3. State-of-the-art algorithms - The models are built on algorithms designed to deliver high accuracy and low latency.
  4. Intelligent field extraction - Get rid of the template based OCR solutions which have to be modified each time you deal with a new data format.
  5. Automation driven solutions - The Nanonets API is designed to drive automation by simplifying workflows and making machine learning more accessible.
  6. Multiple language support - Our OCR engine supports several different languages and can also be trained on new data to build reliable models.
  7. Custom training - training on your own data is many times a requirement for use-cases. Having models built specifically for a use-case means better accuracies, lower errors and less manual review required.
  8. Continuous learning - Your models keep evolving and getting better over time as you feed it new and unique data that your organisation might encounter.

Document Digitization Using Nanonets OCR

What can you digitize?

Deep learning enabled OCR technology allows you to digitize

  1. Forms - legal forms, government procedures, tax fillings, etc.
  2. ID cards - driver’s license, passport, aadhar card, etc.
  3. Legal documents - affidavits, tickets, bonds, etc.
  4. Bank statements - passbooks, account statements, cheques, etc.
  5. KYC information - ID cards, address proof.
  6. License plates - number plates in various languages.
  7. Shipping container numbers - container numbers written in any orientation.

And much more…

How can digitization help?

Digitization of information can help your organisation move towards a paperless workflow. It can help your organisation enable quicker and more convenient processes, enhance customer experience, increase employee satisfaction and reduce costs. It can help you drive better compliance practices in your company while also providing better customer service and increasing the transparency in your organisation.

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