Auto Machine Learning
With the power of Reinforcement Learning, Sia™ auto trains herself in processing the transcripts.
Quick & Accurate
Sia's algorithm analyzes transcripts with 100% accuracy in seconds, with no room for human error.
User Friendly Interface
A simple interface that allows users to upload images or transcripts to analyze, all within the same window.
Why Automated Transcript Processing?
Dealing with thousands of transcripts to process each semester can put a massive load on your Admissions team, not just to process those transcripts, but to do so with no errors while trying to meet deadlines. With an influx of transcript requests to process on time, room for manual/human error increases. Due to the unique nature of every transcript, processing time can be skyrocketing especially with manual efforts where it can take weeks or more, delaying the whole process.
Automation is the key
With out-of-the-box, intricate Image Recognition Algorithms, Sia can read, detect and extract courses, programs, credits & GPA-related metrics from any transcript instantly, with just images of the transcripts.
Neural Vision
Neural vision is the heart of applications that are transforming the way people interact with information. Among its many uses, computer vision is vital for transcribing university transcripts—perhaps the most laborious process in higher education demanding a major time commitment from admissions’ offices, where transcribing records involve both skilled and unskilled workers who use thick manuals to enter data into a database. University clients reach out to us wishing for a way to increase accuracy while decreasing manual work—and do it all more cost-effectively.
OCR
OCR (Optical Character Recognition) technology empowers you to classify & extract data from images, process documents, and other files - reducing errors and improving efficiency in your data management processes. Built with state-of-the-art Artificial Intelligence Technology, Sia’s solution provides digitally accurate character recognition in a variety of file formats, including PDFs, images, and Word documents - and not just in black & white images.
