Technology helps us solve problems we previously thought couldn’t be solved, and we’ve leveraged it in countless ways to improve productivity throughout all areas of our lives. However, technology also has its limitations.
In the context of data capture systems, these limitations make it difficult for artificial intelligence to deviate from strict templates. This makes it very easy for traditional document capture tools to make errors or become confused by a format it hasn’t seen or even a single character that’s out of place.
Machine learning systems design is what makes it possible for Rossum’s data capture solutions to overcome limitations like these.
In this article, we’re going to take you through the challenges associated with traditional data capture technology, the improvements upon this system that machine learning introduces, and the ways Rossum harnesses machine learning to provide better data capture solutions.
What are the challenges of traditional data capture?
Typically, companies process incoming documents (like invoices, packing lists, and purchase orders) using a technology called optical character recognition (OCR). Businesses have been using OCR as the go-to data capture solution for a long time because it works well enough in most cases — but it also comes with some major drawbacks.
Primarily, the problem with data capture systems that rely entirely on OCR is that this technology is really only reliable for scanning documents with no variance in format. In other words, OCR is great for processing documents as long as you’re processing documents that all look the same.
However, as soon as you receive a document that deviates from the standard template the OCR expects to see, problems begin to arise. OCR technology’s ability to capture data accurately diminishes quickly when it’s confronted with document formats it hasn’t been specifically programmed to recognize.
This problem is exacerbated by the fact that most businesses receive documents from many different suppliers using variable document formats. If a business only needed to process documents that look a few different ways, it would be simple to provide the OCR system with a few different templates to work with. However, this is rarely the case.
More often, businesses receive documents with so many different variations in a format that it would be entirely impractical to try to create a separate template to automate data capture for every single one.
Writing thousands of lines of rules for your OCR system completely undermines the point of automating your data capture process in the first place — which is efficiency.
Plus, OCR data capture is a messy process that often results in line-by-line errors, especially if you’re also trying to maintain a vast and complex set of rules to accommodate variable document formats.
Fortunately, there is a better way to approach data capture automation. This is where machine learning design patterns enter the picture.
What are machine learning design patterns?
Machine learning design patterns serve as the foundation of any successful machine learning software. These patterns are used to create algorithms that can learn from data and make predictions about future outcomes.
Machine learning design patterns involve a combination of techniques such as supervised learning, unsupervised learning, reinforcement learning, and deep learning.
- Supervised learning uses labeled data to train a model, while unsupervised learning uses unlabeled data to identify patterns in the data.
- Reinforcement learning is an iterative process where the model learns from its mistakes and improves its performance over time.
- Deep learning is a subset of machine learning that uses artificial neural networks to solve complex problems.
Business leaders should be aware of the importance of machine learning design patterns when considering whether or not to purchase machine learning tools for their businesses. Understanding how these techniques work can help you make informed decisions about which type of machine learning tool best meets your business’s needs.
Additionally, understanding how each design pattern works can help you identify potential risks associated with using machine learning software and develop strategies for mitigating those risks.
What are the best systems for machine learning?
Machine learning systems are becoming increasingly popular in businesses as they offer a range of benefits to data capture processes. These systems use algorithms to analyze large amounts of data, identify patterns and trends, and make predictions about future outcomes.
By leveraging machine learning systems to improve data capture methods, businesses can gain insights into financial metrics and other key indicators that can help them make better decisions.
Machine learning systems can make it easier for businesses to capture data from invoices and other financial documents by automating the process.
By using machine learning algorithms, businesses can quickly and accurately extract important information such as invoice numbers, dates, amounts, and other relevant details. This eliminates the need for manual data entry, which is time-consuming and prone to errors.
Machine learning systems can also improve data capture processes by automating the collection and organization of data from multiple sources. This can help reduce the time it takes for businesses to collect and analyze their data, allowing them to focus on more strategic tasks.
Furthermore, machine learning systems can be trained to recognize specific patterns in documents which makes it easier to identify key pieces of information. These systems are also able to detect anomalies in the data, which may indicate potential problems or opportunities for improvement.
Overall, systems for machine learning provide a number of advantages when it comes to improving data capture processes at businesses. By leveraging these powerful tools, businesses can gain valuable insights while reducing the amount of time required for manual data collection and analysis.
In this way, machine learning systems can help businesses make more informed decisions that will ultimately lead to improved outcomes.
What are some machine learning system design examples?
There are many methods of learning about designing machine learning systems, like referring to a “Designing Machine Learning Systems” PDF.
Examining real-world machine learning system design examples, however, is one of the best ways to understand the practical benefits of using machine learning to improve data capture methods.
Rossum’s fresh approach to data capture represents a significant improvement over traditional methods that rely entirely on optical character recognition. While OCR is a useful tool, its shortcomings mean additional tools are necessary for businesses that hope to achieve a data capture process that’s as efficient as possible.
Rossum’s neural networks are designed to imitate the way humans absorb information from documents — which is much different from the way OCR technology rigidly transcribes information.
The AI uses three steps to capture data accurately:
- Skim Reading – During the first step, Rossum’s AI “skims” the document to quickly get a sense of the layout of the document and where the most important information is located.
- Data Localization – During the second step, the AI narrows in on the areas of the document it believes to contain the information it needs (like the total amount or the name of the supplier on an invoice) and checks to see if it is correct, disregarding any areas it has mistakenly flagged.
- Precise Reading – During the third step, the AI precisely identifies the boundaries of the important information it has located and uses OCR to accurately transcribe that information.
Machine learning systems examples like these make it possible for businesses to improve the efficiency of their data capture processes.
By building machine learning systems that are based on the way human brains process complex documents, Rossum is able to offer a new level of accuracy and efficiency in document data capture.
If you’re looking for an awesome system for machine learning, you should include Rossum in your list of candidates.
What are helpful machine learning system design books?
When it comes to learning how to use machine learning to improve invoice data capture, business leaders should consider reading some machine learning system design books. These kinds of books can provide comprehensive overviews of the best practices for designing and implementing machine learning systems.
One of the most popular machine learning system design books is “Designing Machine Learning Systems with Python” by David Julian.
This book provides an introduction to the fundamentals of machine learning and covers topics such as supervised and unsupervised algorithms, neural networks, deep learning, natural language processing, and more. It also includes practical examples that demonstrate how to apply these concepts in real-world scenarios.
Additionally, “Hands-On Machine Learning with Scikit-Learn and TensorFlow” by Aurélien Géron is a great choice for those looking for a more hands-on approach to understanding machine learning system design.
This book provides step-by-step tutorials on building various types of models using Scikit-Learn and TensorFlow libraries. It also covers topics such as data preprocessing techniques and model evaluation metrics which are essential for developing successful models that can accurately capture invoice data.
Overall, there are many excellent resources available for business leaders who want to learn how to use machine learning to improve invoice data capture.
Investing in one or more of these books can help gain a better understanding of the fundamentals of implementing effective machine learning systems so you can make informed decisions about your own machine learning projects.