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:-
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.
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:-
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.
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 are as follows:-
Conversion accuracy for OCR 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:-
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.
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.
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.
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:-
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:-
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.
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.
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.
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
Optical Character Recognition (OCR) is the technology to convert an image of text into machine-readable text. It is the underlying technology for various data extraction solutions including Intelligent Document Processing. However, OCR is not smart enough to figure out the context in a document - it works simply by distinguishing text pixels from the background and finding a pattern. This limitation could cause inaccuracy in captured data that could directly impact the output of your data extraction model.
Accounts payable is a key financial function for any business. Corporations can have thousands of suppliers; even for relatively smaller businesses, the number of suppliers could be in hundreds. All the invoices they receive from these suppliers come in multiple formats, layouts, and templates - some semi-structured, some unstructured. Therefore, firms expend time and resources to capture invoice information through manual data entry and verification of accounts payable. Manual data entry is not feasible in the long run, definitely not on a large scale. Before we talk about how intelligent invoicing solves the problems associated with manual invoicing, let’s discuss the challenges in much detail.
As most of an organization's information is available in an unstructured format, processing it requires an automated system that can handle documents with minimum human interaction. OCR is one such technology, but its scope is limited as it requires human interaction and is highly dependent on the layout and structure of the document to be processed.These limitations are overcome by Intelligent Data Extraction.Using artificial intelligence, the Intelligent Data Extraction technology extracts data from documents and transforms it into useful information through the extraction process. It functions as a singular tool for extracting information from any type of document and aids in optimizing company operations.