If you have a large scale document-based data integration project, you aren’t stuck choosing between complicated tools like TensorFlow, IBM Watson, Apache Spark, or Rapidminer.
And better yet, you don’t have to build something from scratch to get your machine learning project started.
All you need is an intimate understanding of the documents and data you’re working with.
Intelligent document processing (IDP) is a human-based design approach that achieves better outcomes than any unsupervised machine learning tool. The reason is because unsupervised machine learning tools require very large data sets to work, and don’t work all that well with document-based data.
How do our machine learning tools get better at classifying data? Also, how much work is required to train the data extraction software?
The answer is that it requires a human with significant understanding of their own documents and data. Both improving and training the system is as simple as clicking a button to “tell” the software the document or data type. Learn more about transparent AI in machine learning.
No machine learning tool – not even Amazon, Azure, or Watson – will save you from having to understand your own documents and data. If someone tells you otherwise, they’re lying. If someone you’re talking to believes that IDP (or any other system) will save you from your own document or data problems, they’re setting you up for failure.
Just one look was all it took. IT officials at Oklahoma State University knew just minutes into a demo that Grooper machine learning tools will provide much faster data delivery to systems campus-wide.
They eliminated separator sheets required for manual classification in their previous system – a system that was supposedly “the world’s best.”
And classification is just the tip of the iceberg.
Because Grooper AI acceleration classifies documents from visual appearance and trainable machine learning, rapid integration and accurate data is the new normal.
Improved operational efficiency using machine learning is only possible through visual training.
Grooper machine learning is trained using small sample sets. Built-in visual training data provides quick training and testing. Approved users easily duplicate and adjust pre-trained models to train new document types.
In another government use case, Grooper reduced forms processing times over 95 percent.
Using near-perfect information intelligence through machine learning, the agency’s intelligent document processing is free from templates and slow, manual data entry workflows.
Grooper uses the TF-IDF machine learning algorithm for document classification and data extraction. A visual-based design studio provides the machine learning’s training interface.
Because of this, users aren’t forced to use programming languages like Python, NumPy, Apache Spark, TensorFlow, etc. Although these tools have their place, we’ve made it easier to get results without them.
Here is how our machine learning works, step-by-step:
Training is applied to classify specific document types.
Rapid review and testing is easier for new document types because users immediately see the training effects.
Machine learning also powers data extraction.
“Feature Extractors” in Grooper are paired with easy-to-build regular expression “Value Extractors.”
All training is performed within the same visual interface.
As a result, users choose the correct choice from many similar matches found in the document.
Now it’s easy to find data, and Feature Extractors are the intelligence responsible for accurate data extraction.
Feeding a neural network? As you know, accurate data is required for success. Be confident you have the best data from all available sources.
Grooper’s AI tools easily find any data on structured and unstructured data. Held back by poorly scanned documents? Not a problem with built-in computer vision, over 70 image processing software features, and layered OCR.
Learn more about Grooper’s training-based approach to classification and extraction in this Wiki article.
Companies and government agencies transform operations with Grooper machine learning. This case study shows how machine learning provides:
Thousands of companies choose BIS to enrich products and services with unique data-centric solutions. Here are some of their stories.
Wouldn’t it be great to quickly get the data out of countless documents (no matter the format) and seamlessly put it into your analytics platform? Well, now you can.
Discover a new way that one company is beating the block and beating the competition with document classification software.
Learn how Grooper’s automation is cutting mortgage processing time in half for a large credit union’s 50 branch offices.
We are proud to announce that Grooper software, as well as all software products under the BIS brand, is 100% Made in the USA. Every line of code, every feature, and every update stems from our dedicated team working diligently at our Oklahoma City headquarters. Additionally, our support services are exclusively provided by local talent based in our Headquarters office, ensuring that you receive firsthand, quality assistance every time. Our unwavering commitment to local expertise emphasizes our dedication to top-tier quality and innovation. Thank you for your continued trust in our homegrown solutions.
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