Intelligent Character Recognition (ICR), Optical Character Recognition (OCR) and machine learning based corrections of data transcription from scanned business documents

SS&C processes more than 80% of financial scanned and faxed documents in the US and requires large amount of manual labor in order to map information from a document into another form. Advances in neural networks applied to computer vision have produced text detection and recognition that nears human performance. This project will be leveraging these approaches to address the main challenge of applying image segmentation and character recognition techniques to large volumes of documents, namely the sensitivity of the process to phenomena like the variability of text, document formats and imaging conditions. The expected benefits of the project to the industrial partner are (i) reduction of human error in the document workflow, and (ii) faster turn-around times for customers of SS&C.

Intern: 
Chen Chen
Faculty Supervisor: 
Joseph Jay Williams
Province: 
Ontario
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