Parallax Advanced Research and ACT3 create AI-powered transcribing solution
Parallax Advanced Research and the Air Force Research Laboratory Autonomy Capability Team 3, or ACT3, are creating a novel computer-based machine learning technology named Detextron.
The innovation extracts information from complex documents containing signatures, lines of text, and images and processes and transcribes it into a readable report.
Detextron will first be used by ACT3 customers, such as the U.S. Office of Information Policy for fulfilling Freedom of Information Act requests.
From layout to format to the type of content contained with them, documents can be complex. They can include a mixture of typed and handwritten text and embedded and scanned items like images, audio, and video.
Detextron might provide a more rapid solution to sorting this information, according to the companies. The technology is based on a neural network being developed by Parallax Advanced Research, a nonprofit research organization and the Air Force Research Laboratory, both headquartered in Dayton, Ohio. Leading the development from Parallax is Software Developer and Natural Language Processing Expert Vahid Eyorokon.
Eyorokon used various sources to build the dataset, including a separate neural network that generates handwritten text from the open-source GitHub repository. The last dataset he generated contained 800,000 images, 124 million lines of text, 5 million lines of synthesized script, and 75,000 dynamic document layouts.
“The question is: how do you extract cohesive information from a complex document? That's the question I'm solving” Eyorokon said. “As I started exploring answers, I realized that we could create a model that could do three things: detect, classify, and segment information within documents which can contain a combination of images, handwritten text, and typed text, and extract the content into a cohesive report.”
The model functions in three steps: detection, classification, and segmentation. Detection requires an end-user to upload documents into Detextron, then the program selects its content on a screen by drawing a bounding box around each line of text and script.
Classification involves the algorithm to distinguish the selected content between text, script, and images. Segmentation involves the algorithm to use a polygon that outlines text and identifies its pixels that correspond with a text, script, or image.
Now, Detextron will be further refined to process, classify, and transcribe written text exactly as it’s written. Written analysis is a complicated process because every individual has a unique way of writing as well as one’s handwriting evolves over time.
In addition, Detextron will eventually be able to generate reports on documents in languages other than English and on false documents, which are documents created with a sense of authenticity and appear to be factual but are not.
Detextron is one of many artificial intelligence projects conducted by Parallax Advanced Research scientists that deliver innovative solutions to academic, industry and government clients across the United States.
For more information about this news, visit www.parallaxresearch.org.