Data Extraction

Invoice Data Extraction using OCR

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Invoice Data Extraction using OCR

Invoice processing is done 2 ways: manual and automated. The manual invoice processing requires inputting information, confirming accuracy, and archiving documentation. On the other hand, Optical Character Recognition (OCR), a text-extracting technique is used to automate invoice processing that turns digitized documents into files that can be edited. In automated invoice processing, invoices are unit routed through centralized points of entry in an automatic system, therefore whether invoices arrive via email or other sources, the system processes them. This frees up time for team members to focus on more specialized jobs.

What exactly is invoice OCR processing and how does it work? Let’s find out in this blog.

Let’s jump right into it:-

What is OCR invoicing?

OCR invoicing is the process of training a template-based OCR model for specific invoice layouts, setting up input paths for these invoices, extracting data, and integrating the extracted data with a structured database. 

This semi-automated data extraction technique can be used to extract field specific information from fixed template documents. OCR is not able to extract “context specific” information from documents - this limitation is overcome by Intelligent Document Processing.

How does OCR invoicing work?

Extracting info from invoices is tough since no two invoices are alike. When it comes to automated data extraction from invoices, businesses struggle to line up software system exploitation templates. As a result, there are numerous exceptions. Before we talk about the limitations of OCR invoicing, let's first talk about its flow:-

Flow of OCR invoice processing:-

  1. Preprocessing of the Invoice Image
  2. Text Detection
  3. Text Recognition
  4. Text Extraction/ Information Extraction
OCR invoicing processing workflow

1. Image Pre-processing

In this step, non-scanned PDF invoices are scanned and turned to JPG files with a resolution of 600x600x3 and 300 DPI. In this step, multiple pre-processing techniques are applied. After we are done processing all the images, they are parsed to deep learning model for training.

2. Text Detection

Once we have the complete data, it is fed to the detection model, which can recognise the tables, paragraphs, and forms within the input images.

3. Text Recognition

This step involves identifying the location of one or more objects/text and their extent are delineated by bounding boxes.

4. Texts/Information Extraction

Now the next step is to extract the text from the detected regions here we are currently using Tesseract-OCR, an open source programme which is use to extract data from the images.

There are several alternative ways that can be used for OCR invoicing.

  1. Deep learning technique for region detection. 
  2. OCR tool for text extraction from the discovered region.
  3. Use text analytic to determine the relationship between the extracted texts and save them to a database.

Limitation of OCR invoicing

OCR, not being fully automated, poses several limitations to extract invoice data and thereby complicate automation implementation. Data extracted for the first time from a new invoice format from an OCR software cannot be 100% correct. The reason being different invoice templates and styles which requires initial familiarity of the system with the invoice template.

Limitations of OCR invoicing

Limitations of OCR invoicing are as follows:-

  1. Inaccuracy at field level can be as high as 30%.
  2. Depending on the solution, human intervention may be required from feeding the invoice into the solution to reviewing the extracted data.
  3. OCR finds it difficult to process sub-standard quality images and extract data.
  4. It doesn’t work efficiently for handwritten characters.

How accurate are OCR invoicing solutions?

OCR accuracy is obviously essential, and most OCR software are able to deliver 98 to 99 percent accuracy at the page level. This means that 980 to 990 characters in a 1,000-character page will be accurate. This degree of accuracy is sufficient in most instances. But, does full-page OCR accuracy of 98 to 99 percent translate to acceptable data extraction accuracy from these documents? Certainly not.

If you require 99.9% accuracy at the data field level, depending on 99% correctness at the page level might mean disaster. In the instance of our 1,000-character page, even if an OCR engine achieves 99 percent accuracy at the page level, what if the ten inaccurate characters are among ten of the 20 metadata needed by the business? This accuracy of 99 percent suddenly lowers the field level accuracy to 50 percent. 

This is where field-level accuracy, as assessed by the field-level confidence score, enters the picture. Keep in mind that page-level accuracy scores are frequently based on high-quality scans.

Some of the best and most popular OCR solutions that are available in the industry are as follow:-

  1. Tesseract OCR
  2. Docsumo
  3. ABBBY FineReader
  4. Google Cloud Vision

1. Tesseract OCR

Tesseract is a command-line OCR engine programme developed by Hewlett-Packard. Its use is significantly simplified by pytesseract, a Python wrapper. There is also a graphical user interface frontend gImageReader, so you may choose the one that best meets your requirements. 

2. Docsumo

Docsumo is an automated document processing solution that helps businesses  accurately extract data from multiple documents without any manual setup. The automated data extraction solution is able to process invoices, bank statements, identity documents, contracts, forms, insurance applications and many more.

3. ABBBY FineReader

ABBBY can extract text from some of the most popular image formats, including PNG, JPG, BMP, and TIFF. All you have to do is upload a high-resolution image or file for the programme to analyse. When dealing with complex financial documents, ABBBY requires additional post-processing for domain-specific keywords.

4. Google Cloud Vision API

Google Vision OCR does not support documents larger than 10 MB in size. It performs similarly to ABBBY FineReader on scanned emails and text in smartphone-captured documents. However, it recognises handwriting much better than Tesseract or ABBBy.

Below are the image processing results:-

Comparison table of OCR solutions

OCR invoicing with Machine Learning

Machine learning is a subclass of artificial intelligence that refers to software programmes' capacity to solve ongoing issues by analysing data without (or with minimum) operator interference. It lies at the heart of automated invoice capture software.

OCR and Machine Learning work hand in hand to produce a powerful system. OCR extracts the data, while machine learning hunts for patterns in the structure of an invoice; when combined, they can discriminate the data, such as understanding the difference between an address number and the amount owed. 

Machine learning has enabled the automation of tasks that previously required manual supervision:-

  • Assign the correct general ledger codes to a certain vendor or transaction type.
  • Enter invoice information (i.e., invoice number, supplier identity, and total amount) into the automated accounts payable system to process payments.
  • Without any human intervention, send the invoice to the relevant approver for signing. 

Furthermore, machine learning enables organisations to quickly implement an automated solution. Previously, establishing an automated accounts payable process needed the development of rule-based logic prior to the launch of a new platform. Machine learning now enables AP software to learn workflow logic on the job, that is, while it processes invoices.

Invoicing OCR with Machine Learning in action

We’ll analyze the OCR plus Machine Learning invoicing with an example. In this example, we'll look at the data collected from an invoice. 

Assume we want to collect the tax. We'll start by collecting 100 invoices for the data training phase. Next, the data scientist will use neural networks and deep learning to instruct the algorithms on where to hunt for the "tax." This method will be used on 80 invoices, with the remaining 20 utilised in the part that follows.

It is now up to the algorithm to get to work after training the systems on where to seek for the tax. On the remaining 20 bills, the algorithm's duty will be to figure out where the data that it learned is, and we're now in the data testing stage.

Finally, there is one more step to complete, which is data validation. This is the phase in which the human will approve the work of the computer, as the name indicates. Indeed, at the conclusion of the data test move, the algorithm will announce an accuracy percentage, allowing us to choose whether it is appropriate for us. Assume that the precision is 70%. The data scientist can either accept this level of accuracy or keep training and repeating operations until a more precise OCR is achieved.

Why Docsumo?

Using Docsumo's sophisticated OCR engine and APIs, you can automate invoice data capture and processing. Minimal configuration, smart extraction and validation, along with simple integration. Save hours formerly spent manually entering invoice data by using Docsumo's invoice capture software.

In real-time, extract data from thousands of invoices with greater than 99% accuracy. We assist you in detecting duplicate invoices, identifying mistakes, and detecting potential fraud with the aid of sophisticated accounts payable automation.

1. Documents are processed in less than 2 minutes

Allow your operations team to be free of repetitive and redundant activities with Docsumo’s automated accounts payable solution. Help your team save time and redirect your team's attention to more strategic projects. 

2. Reduce document processing and back office costs by up to 50%.

Docsumo APIs for document classification, key attribute identification, and data verification save your team from tedious paperwork and reduce the number of paid hours they previously spent on these tasks. This means you can utilise the cash to grow your business.

3. Adhering to ease of compliance standards

With automated invoicing, assessing your organisation and maintaining compliance requirements is a breeze. Automation makes it easy for both your company and the auditor to manage invoices and compare them to purchasing history. Furthermore, automation decreases the risk of lost or missing bills, misfiled invoices, and human mistake substantially. 

4. Simplicity

Automation helps you to collect the invoice digitally and transfer it to the cloud which allows anybody to view quickly from anywhere. The is usually helpful if your company has overseas accounts that needs real time access to the invoices

5. Customization

You may quickly and simply construct a customised accounts payable flowchart and re-direct invoices in a certain sequence. You may also provide various personnel in your accounts department with varied levels of access. Automated invoicing software is adaptable and may be modified to meet the needs of your business or corporation.

6. Minimising fraud & risk

Businesses are frequently exposed to the risk of fraud as a result of bogus or fraudulent invoices intended to influence profitability.  The likelihood of fraud is reduced when invoices are automated. When invoices are automatically recorded, they are also validated and cross-checked against purchases. Furthermore, a comprehensive database of invoices is produced, leaving almost no space for forgery or fraud. Plus Docsumo's real time document fraud detection ensures no tampered invoice gets into your system at all.

Suggested Case Study
Automating Portfolio Management for Westland Real Estate Group
The portfolio includes 14,000 units across all divisions across Los Angeles County, Orange County, and Inland Empire.
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Pankaj Tripathi
Written by
Pankaj Tripathi

Helping enterprises capture data for analytics and decisioning

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