Optical Character Recognition (OCR) Technology for Invoice Capture

Introduction

OCR has been around for many years. Probably longer than you would guess. In 1914 Emanuel Goldberg developed a machine that could read characters and convert them to telegraph code. Since then there has been a lot of development in the field of OCR technology. This article will discuss how it has evolved to assist accounts payable staff in processing invoices.

What is OCR and how does it work?

OCR, or Optical Character Recognition, is a technology that converts typed or printed text into a digital form that can be edited and searched. It works by using algorithms to identify and interpret characters or symbols from an image or a scanned document. Firstly, the OCR software scans the image and identifies each individual character by analyzing their shapes and patterns. It then compares these characters with a database of known characters to determine their most likely identity. The software also makes use of language patterns and contextual cues to increase accuracy in identifying words and phrases. Optical Character Recognition has evolved over the years and has become much more advanced with the advancements in technology. Today, OCR is used in various applications like digitizing documents, creating searchable text from printed materials, and extracting data from business cards. It has simplified and automated tasks that were previously time-consuming, allowing for more efficient and productive workflows. 

How Does Invoice OCR Work?  

Traditionally invoices were printed and mailed to the accounting department. Then the process would start with invoice scanning. Then the data was captured by manual data entry.  OCR was introduced during the scanning process to reduce the amount of manual data entry. 

Today, invoice OCR, or Optical Character Recognition, converts data from printed or handwritten invoices into editable and searchable formats. The process of how it works involves several steps. First, the invoice is scanned or captured with a digital device, such as a scanner. Then, the OCR software analyzes the image and identifies matches the shapes and patterns of the characters against a database of known characters. Once the text is recognized, the OCR software converts it into machine-readable data, which can be exported to other software or systems.

Pattern Matching

Invoice capture systems use pattern matching as a crucial technique to identify and extract relevant data from invoices. This process involves training the system to recognize specific patterns, structures, and formats that are commonly found in invoices. Here’s how pattern matching is typically employed in invoice capture:

1. Template Recognition: Invoice templates often follow consistent layouts with fixed positions for key information like vendor name, date, invoice number, and line items. Pattern matching involves creating templates or predefined patterns for various types of invoices. The system then matches the incoming invoice against these templates to identify and extract relevant data accurately.
2. Regular Expressions: Regular expressions are powerful tools for defining and recognizing patterns in text. For instance, an invoice capture system can use regular expressions to identify email addresses, phone numbers, or specific codes on invoices. For example, searching for a phone number, the system would look for a string of numbers with a pattern of 3 digits a dash, another 3 digits, and 4 digits. If the software found text that matched that format, it would assume that this is a phone number. And since there are several formats in which a phone number can be written, The system would try to match the text to each of the patterns.  These expressions enable the system to locate and extract important data based on predefined patterns.
3. Keyword Detection: Many invoices contain specific keywords or phrases that indicate certain data fields. By identifying these keywords using pattern matching, the system can accurately extract relevant information. For instance, the word “Total” might indicate the total amount due, and the system can be trained to recognize this pattern.
4. Positional Recognition: Certain data elements consistently appear in specific positions within an invoice. Pattern-matching algorithms can be designed to look for information in these fixed positions. For instance, the invoice number might always appear in the top right corner, making it a recognizable pattern.
5. Contextual Analysis: Pattern matching isn’t limited to individual characters or strings—it can also involve recognizing the context in which certain data appears. For example, the system might identify the total amount due by locating the word “Total” followed by a monetary value, even if the exact format varies.

What Invoice Data is Typically Captured?

This data extraction process often includes extracting key information such as vendor name, vendor number, invoice number, PO Number, due date, and amount owed. Some systems will attempt to capture the line item data, although this is more difficult.

What are the Benefits of Using OCR in AP Processes?

Using Optical Character Recognition (OCR) technology in Accounts Payable (AP) processes has numerous benefits.

1. It greatly improves the efficiency of data entry. OCR software can quickly and accurately extract relevant information from invoices, receipts, and other financial documents,
2. Reducing manual data entry. 
3. Saves time
4. Reduces the risk of human error.
5. Improves the ability to pay bills in a timely manner. 

How Accurate Is OCR Scanning?

OCR (Optical Character Recognition) scanning technology has come a long way over the years, but its accuracy still remains somewhat variable. While OCR software is designed to read and convert printed or handwritten text into digital format, it is not always foolproof. The accuracy of OCR scanning largely depends on factors such as the quality of the document being scanned, the font type, and the clarity of the text. Modern OCR systems are generally quite accurate when it comes to scanning printed text that is clear and well-formed, achieving accuracy rates of 90% or higher using the best OCR engines. However, difficulties arise when dealing with complex fonts, heavily stylized writing, or documents with low-quality scans. In such cases, errors and inaccuracies can occur, requiring manual proofreading and corrections. Despite its limitations, OCR scanning remains a valuable tool for digitizing large volumes of text, especially when combined with human oversight and verification to ensure a high level of accuracy. 

But typically invoice OCR capture accuracy is not that high because of a number of challenges which we will discuss in the next section.

What Are Some Common Challenges With Invoice OCR?

One of the common challenges with invoice OCR is not the accuracy of OCR’ing the text but text extraction. The pattern recognition algorithms fail to correctly capture the necessary invoice data because:

1. There is nearly an infinite number of invoice formats or layouts to deal with.
2. Additionally, the presence of logos, stamps, or background images on invoices can also pose a challenge for OCR systems, as they may interfere with the accurate extraction of text.
3. Lines and boxes around the text can cause problems especially where the text touches them.
4. There is a wide variety of keywords that can be used which makes it difficult to handle. Such as the text adjacent to the invoice total could be Amount Due, Total, Total Due, Amount Charged, Invoice Balance, Invoice Amount, Invoice Total, Due Upon Receipt, or even no text at all. In addition, this text can be above the dollar amount, above, or below.
5. to convert image-based invoices into machine-readable text. However, factors such as poor image quality, complex invoice layouts, and handwritten or distorted fonts can lead to inaccuracies in the extracted data. Another challenge is the identification and handling of different invoice types. Invoices can vary in format, language, and content, making it difficult for OCR systems to correctly interpret and extract the relevant information. Furthermore, OCR software may struggle with data extraction when invoices contain tables or graphs, as these elements require more advanced image processing techniques.  Overall, while OCR technology has made significant advancements in recent years, these challenges highlight the need for continuous development and improvement in order to achieve high accuracy and reliability in invoice processing. 

Why OCR is not enough to transform your invoice processing

Invoice optical character recognition software allows the invoice capture process to approach about 80% accuracy because it tries to handle all the different invoice formats. Eighty percent is great, it saves a lot of time over manual invoice processing. If you want to reduce the need for manual data entry, the AP automation software needs to do data validation on the capture data.

Data validation can compare the data from an invoice to data in the ERP system. For example, if the software captured the PO number, the processing software could look up the PO number and see if the vendor name and number match what is in the accounting software.  I think that with clever software applications invoice data extraction accuracy can approach 90% accuracy. But if your accounts payable department would like to get more accuracy, then you need to look at artificial intelligence solutions.

Increasing Your Accuracy Rate With AI and Machine Learning 

In today’s fast-paced business world, accuracy is crucial. Every company strives to have efficient and error-free processes, especially when it comes to invoice capture. With the advancements in technology, artificial intelligence (AI) and machine learning have proven to be game changers in this area. By implementing AI and machine learning algorithms, businesses can significantly increase their invoice capture accuracy rates. These technologies can automatically extract and interpret relevant information from invoices, eliminating the need for manual data entry and reducing errors caused by human intervention. AI and machine learning programs are constantly learning and improving as they process more data, making them even more accurate over time. They can recognize patterns and adapt to new formats, ensuring that even invoices with unique layouts or structures are accurately captured. Furthermore, these technologies can perform data validation checks, flagging any discrepancies or anomalies, further enhancing accuracy. Ultimately, harnessing the power of AI and machine learning in invoice capture can streamline business processes, save time, and reduce costs associated with manual data entry and error correction. 

I have done a lot of testing of AI capture software solutions and have found some of them getting very good results with invoice recognition. And even handwritten invoices are getting good results,  to extract data from invoices. Not all AI capture solutions are that accurate, but I have found a few that are approaching 100% accuracy.

What Can You Do With Invoice Data Once It Is Collected Correctly?

Once you automate invoice capture so that you are getting real accurate results, then there are many things you can do to improve the accounts payable process and nearly eliminate manual processing. The next step to process an invoice is to automatically route the invoice to the approvers. Then the software can be used to push the data into your ERP, thus saving more manual data entry. Normally the data entry task of entering every invoice into the ERP or accounting software is a very time consuming task. However, the invoice automation software can now accurately transfer invoice data without manual intervention. And if your processing solutions has the ability, it could move the invoice with metadata to your document repository such as Laserfiche.

Conclusion

Certainly, Invoice OCR capture has come a long way in the past 10 years. Solutions that read paper invoices or digital invoices, OCR invoices, and use pattern matching techniques are a huge improvement over the old way of doing things. But, if you’re processing a lot of documents you will still have quite a number to manually process. When you add a good AI solution to your OCR invoice processing, the number of invoices you have to touch. It greatly reduces your cost per invoice, frees up valuable time for your accounting team, and reduces the processing time so you can pay your vendors in a timely manner.

If you would like to learn more about how this technology could help your organization, please contact me.

Larry Phelps

Hemingway Solutions

612-382-4069 or lphelps@hemingwaysolutions.net