How AI image processing works

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AI image processing

In 2020, Automation Anywhere commissioned a global study of more than 10,000 office workers, revealing the world’s most hated administrative task – manual data entry. Manual data entry is a legacy workflow that many businesses continue to rely on despite its many flaws. 80% of the time, a document arrives at a business as part of a core process; it is in an unstructured format such as an image or PDF document. 

These formats, while being useful methods of sharing documents back and forth between professionals, are not useful to computer systems. Manual data entry is the process of literally typing the unstructured data in these files into computer systems and applications where it can then be processed. This soul-crushing task unnecessarily complicates a variety of business processes across industries. Whether it’s processing invoices for the AP team, or handling packaging lists, nobody likes having to manually type data into a computer. This is exactly the kind of mindless task that can leave your employees demotivated and potentially looking for another position. 

However, this data is essential to the success of your business. In order to pay your invoices and keep your vendors in a good relationship with you, your accounting or ERP system needs the data in those invoices. Failing to capture the data is not an option, so many organizations just press on, continuing to hire more and more data analysis and data entry professionals as the volume of data collected grows. 

Fortunately, it doesn’t have to be like this. 

AI image processing can be used to automatically extract the data from images and PDFs of business documents and export it to its desired destination. The technology is called cognitive OCR (optical character recognition) and scans the image for characters that it can associate with fields and turn into structured data. There are several image processing techniques. Some use AI, and others do not. For example, template-based OCR relies on pre-defined rules and templates instead of machine learning algorithms. By looking at image processing AI examples, you’ll see how cognitive OCR technology can rescue your document-based processes from the pitfalls of manual data entry and set you and your teams on a path to success. 

How does AI image recognition work?

What we’ve mentioned about the benefits of AI-enabled OCR may sound good, but you’re probably wondering: “how does AI image recognition work?” At a high level, AI image data capture relies on neural networks and machine learning algorithms to recognize document types and extract the data within them. You may have heard of “training” AI systems to be able to learn to do tasks. This training is essential to the success of any AI system. In the case of image processing tasks, machine learning systems are simply fed thousands of different images of documents, each of which is labeled. Then, by extracting all kinds of data points from each image, the system begins to build categories so that it can even identify a document format it has never seen before as “invoice” or something else. The key to this learning capability is neural networks. Neural networks are a technological innovation that allows computers to “think” and “learn” much in the same manner as humans do. In a neural network, the nodes are deployed in a similar manner to the neurons in our brains. Building an intelligent document processing (IDP) system with AI-enabled OCR at its core is a difficult task and different organizations have taken different approaches as they work towards the ultimate goal of eliminating the drudgery of manual data entry. 

Rossum is an IDP platform that uses neural networks to perform AI image recognition that can automate all kinds of business tasks, such as processing invoices. Our platform works by mimicking the way humans read documents. Studies have shown that humans tend to skim through a document to just get the basic textual context before moving into a more precise reading and actually identifying the data. This is how we’ve designed the Rossum engine

During its first pass, it simply identifies the regions of importance on the document and maps them in space. It then uses this spatial information to compare with document formats to help it understand what document it’s looking for. All of the phases of the Rossum system are designed to give it as much context as possible before actually extracting the data. This context is what enables the data capture engine to be so accurate. Does it work? Yes. The Rossum platform provides between 80% and 90% accuracy for a new user and regularly reaches 95% accuracy within the first month of routine usage. 

Machine learning in image processing

The reason so many companies have continued to rely on manual data entry is that few machines have the capacity to efficiently extract data from images and learn image formats as well as the human brain. The best machine learning philosophies acknowledge the superiority of the brain and try to mimic its functions with more scalable technologies. 

Machine learning is one of the critical components of AI image processing. As we have already mentioned, AI systems need to “learn” what document types are so that they can correctly and rapidly extract the data within documents as they receive them. This is also what is meant by the term machine learning image classification. The context the system gets from knowing the type of document can give it the ability to run validity and accuracy checks before capturing and exporting the data. Machine learning in image processing provides automation. Template-based systems are expensive to maintain because they require experts to regularly write new rules and templates as your company grows and works with more vendors. 

With a machine-learning-enabled system like Rossum, maintenance is done automatically by the AI. Furthermore, because systems don’t need to sleep, Rossum can continue learning 24/7. With each new document, our clients from all over the world are improving Rossum’s capabilities and making data capture truly automatic. 

Is image processing part of machine learning? No. There are systems that can scan and identify images and data within those images without the use of AI. However, AI brings more accuracy, speed, and scale so that large enterprises can free up their employees’ time to focus on tasks that require more creativity and can grow your business. One of the features that make the Rossum machine-learning platform unique is its setup-less data capture. Because it is a cloud-based platform and can be accessed from anywhere, you don’t need to rely on any expensive infrastructure. Plus, its machine learning capabilities mean that it can start capturing data straight out of the box. 

AI image recognition software

There are many different options for companies looking for AI image recognition software. There are even AI image processing Python libraries that your engineers could use to build your own tool. However, this can be a very expensive and time-consuming option. Why not rely on an AI image recognition platform for your data capture needs like Rossum? Rossum features an extremely easy-to-use interface that enables you to perform batch processing. With just a few clicks, you can extract data from hundreds of images automatically.

Furthermore, as it is more than just an image scanning solution, Rossum possesses the capability to ingest documents from a variety of different channels, providing one central place to process all your documents. Some businesses may feel some hesitation in relying on a cloud-based solution for processing sensitive data. However, cloud-based automation shouldn’t mean a loss of control or security. Our platform is ISO27001 certified and HIPAA compliant to ensure that your data is kept secure at all times and features all kinds of analytics so that you can have peace of mind. 

AI image processing software

The data capture problem is real. Employees are fed up with having to deal with tasks that were never on their job descriptions. Businesses are tired of paying the high costs of manual tasks that don’t offer any kind of ROI. With the right kind of automation software, you could convert departments like accounts payable from cost centers to profit producers. Many people think that AI is going to replace their jobs. We don’t believe in that philosophy. The Rossum IDP solution has been designed with people in mind from the very beginning. 

Our goal is to provide a solution that frees human employees from repetitive tasks and enables them to use their time and creativity to contribute more to their industries. This is reflected in the easy-to-use validation interface we designed that ships as part of the platform. When comparing AI image processing software platforms, it’s important to read reviews and understand what the company can truly offer. Rossum makes it easy to learn more about the various industries and companies we have worked with here. An AI image processing solution should be versatile. We have mentioned invoices repeatedly throughout this discussion. Rossum can do much more than just process invoices. Our platform can be used effectively throughout a variety of industries and in any process that is document-reliant. 

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