BYU

Abstract by Taylor Archibald

Personal Infomation


Presenter's Name

Taylor Archibald

Co-Presenters

Mason Poggemann

Degree Level

Masters

Co-Authors

Mason Poggemann

Abstract Infomation


Department

Computer Science

Faculty Advisor

Tony Martinez

Title

Scan, Attend, Recover: A Deep Encoder-Decoder Network to Recover Handwritten Strokes from Images

Abstract

Stroke order and velocity are helpful features in the fields of signature verification, handwriting recognition, and handwriting synthesis. We use a deep neural network (DNN) encoder-decoder architecture with attention to infer temporal stroke information from static images of handwritten text. We believe this is the first DNN approach to stroke recovery that accommodates variable width inputs. We further propose several loss functions that are novel for this task. Finally, we demonstrate that our recovered temporal stroke information can improve both handwriting recognition and handwriting synthesis.