What is AI image processing?
Image processing is the analysis and manipulation of a digitized image, often to improve its quality.
By leveraging machine learning, Artificial intelligence (AI) processes an image, improving the quality of an image based on the algorithm’s “experience” or depth of knowledge.
For example, if you wanted to improve the quality of an image of a turnip, you’d want to know what a turnip looks like.
Machine learning becomes increasingly accurate over time given a greater sample size. In this example, the more instances of a turnip you feed to a computer, the more precise its resulting processed image of a turnip will be.
How AI image processing works
In the modern world, companies and organizations around the world use AI image processing for a variety of applications—including character recognition, data extraction, pattern recognition, visualization, identification, and classification, among others.
Google, for instance, uses existing photos that get uploaded to the web to train its algorithm, which can then identify new images better and help people find more accurate search results. This common AI use-case partly explains why you often have to click on a fire hydrant or crosswalk when you log in to many websites
AI image processing: a hidden time saver
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; more often than not, documents enter an enterprise’s workflow in an unstructured format, such as an image or PDF document. This may be an easy job for AI image processing solutions, but remains a significant headache for teams and employees who still perform manual data entry.
Unstructured data formats, while being valuable methods of sharing documents back and forth between professionals, are not necessarily compatible with computer systems. Manual data entry requires the unstructured data contained in these files to be typed into computer systems and applications where it can then be properly processed.
This time-consuming task drains a company’s resources and unnecessarily complicates a variety of business processes across industries. Whether it’s processing invoices for the AP team, or handling packaging lists, most backoffices would rather not have to manually type data into a computer. This is precisely the kind of administrative task that can leave your employees demotivated and potentially looking for another position.
However, this data, whether unstructured or not, remains essential to the success of your business. To pay your invoices and maintain excellent relationships with your vendors, your accounting or ERP system needs the data in those invoices.
Failing to capture the data is not an option. As a result, many organizations just press on—and are often caught playing from behind. For organizations still relying on manual data entry, a common pattern ends up repeating itself.
As the volume of data collected grows, instead of turning towards an automated, AI-driven solution, some companies continue to hire more and more data analysts and data entry professionals in vain.
Fortunately, it doesn’t have to be like this.
AI image processing solutions like Rossum can automatically extract the data from images, as well as PDFs of business documents, and export data to its desired destination. The more popular AI-driven data processing technology is called cognitive OCR (Optical Character Recognition). Cognitive OCR technologies scan an image’s 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 predefined rules and templates instead of machine learning algorithms. By looking at image-processing AI examples, you’ll start to see the true value of cognitive OCR technology—and how it can not only rescue your document-based processes from the pitfalls of manual data entry, but set you and your teams on a path to sustained success.
How does AI image recognition work?
While it may be tough to argue against the benefits of AI-enabled OCR, you may be left still wondering about a simple, yet related, question: 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. In this context, you may have heard of organizations “training” AI systems to learn how to perform certain tasks. This training remains essential to the success of any AI system.
With respect to image processing tasks, machine learning systems are typically fed thousands of different images of documents, each of which are labeled. Then, by extracting all kinds of data points from each image, the system begins to build more granular categories. Powerful data aggregation ultimately allows AI-based image processors to identify document formats it may have never come across before.
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.
The nodes in a neural network are deployed similarly to the neurons in our brains. Building an intelligent document processing (IDP) system with AI-enabled OCR at its core is a difficult task. Different organizations have taken different approaches, to be sure, as they work towards the ultimate goal of eliminating the inefficiencies stemming from manual data entry.
Rossum is a powerful, enterprise-grade IDP platform that uses neural networks to perform AI image recognition. Our solutions drive corporate bottom-lines by automating all kinds of business tasks, such as processing invoices and other administrative data work.
Our platform works by mimicking the way humans read documents. Studies have shown that humans tend to skim through a document just to get the basic textual context before subsequently moving into a more precise reading and identifying the data. We’ve designed the Rossum engine based on this approach.
During its first pass, Rossum’s AI-driven image processor simply identifies the regions of importance on the document and maps them in space.
It then compares this spatial information with matching document formats so the system knows what document it’s looking for.
All of the phases of the Rossum system are designed to provide 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% to 90% accuracy for new users, and regularly reaches 95% 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 can efficiently extract data from images and learn image formats as they relate to the human brain. The best machine learning philosophies acknowledge the superiority of the human 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 mentioned, AI systems need to first “learn” what document types are in order to correctly and rapidly extract valuable data as they receive documents in real time.
This phenomenon can also be referred to as machine learning image classification. The context the system gets from knowing the document type gives it the ability to run verification and accuracy checks before capturing and exporting the data.
Machine learning in image processing runs on automation. As your company grows and builds out its vendor network, template-based systems become expensive and time-consuming since they require experts to regularly write new rules, code, and templates.
With a machine-learning-enabled system like Rossum, maintenance is automatically performed by the AI. Furthermore, because our systems don’t need to sleep, Rossum’s solutions continue learning 24/7. With each new document, our clients from all over the world end up improving Rossum’s capabilities and making data capture genuinely automatic.
So…is image processing part of machine learning? Not necessarily.
It’s true that some systems have the ability to scan images and identify data within those images without relying on AI. However, AI brings more accuracy, speed, and scale to data capture and processing—given the myriad of revenue-driving benefits, it’s no surprise that large, global enterprises are increasingly turning to the latest AI-based image processing solutions.
By deploying Rossum’s cutting-edge AI-based solutions, you can free up your employees’ time—allowing them to focus more on value-add tasks that expand on your business’ core value proposition.
One of the features that separates Rossum’s machine-learning platform is its setup-free data capture. We offer a cloud-based platform that can be accessed from anywhere in the world. With Rossum, you never have to worry about relying on expensive infrastructure. With minimal lift required, its machine learning capabilities mean that it can start capturing data straight out of the box.
AI image recognition software
There are many options on the market for companies looking for AI image recognition software. There are even AI image processing Python libraries that enable your own engineers to build your tool.
Of course, this would be a costly and time-consuming option. Instead of creating more development work for your engineers, why not rely on a specialized and proven AI image recognition platform for your data capture needs like Rossum?
Rossum features an extremely easy-to-use interface that powers highly-precise, rapid batch processing. With just a few simple clicks, you can extract data from hundreds of images automatically.
What’s more, Rossum is more than just an AI-driven image scanning solution. Rossum possesses the capability to ingest documents from a variety of different channels, serving as a centralized processing hub for all of your documents.
Some businesses may be skeptical when trusting a cloud-based solution to processing sensitive data. However, cloud-based automation doesn’t mean a loss of control or security—far from it. Our platform, which is ISO27001 certified, SOCII Type 2 compliant, and HIPAA compliant, bolsters your data security to give you peace of mind.
AI image processing software
The data capture problem is real.
Employees are fed up with tasks that were never outlined in their job descriptions. Businesses, meanwhile, are tired of paying the high costs associated with manual processing tasks that fail to generate ROI. With the right automation software, you can seamlessly convert departments like accounts payable from cost centers to revenue producers.
Many people fear that AI is going to replace their jobs. We don’t believe in that philosophy. In fact, 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. In doing so, businesses empower their employees to maximize their value-add contributions to the firm. This is reflected in the easy-to-use validation interface we designed that ships as part of the platform. When comparing the various AI image processing software platforms out there, it’s essential to read reviews and understand what the company can offer.
Rossum makes it easy for you to learn more about our proven track record across various industries and companies straight from the source. An AI image processing solution should by definition be versatile.
So whether your processing needs start with invoices and build over time, the good news is that Rossum does much more than simply process invoices. Rossum’s all-in-one platform can be used effectively throughout a variety of industries to upgrade any document-related process.