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Nicholas R. Howe
Bachelor of Arts
Handwriting recognition, Machine learning, Neural networks, Attention, Spatial transformer networks, Viterbi algorithm, Encoder-decoder
Offline handwriting recognition systems aim to automate the creation of machine- readable text transcriptions from images of handwritten data. Current state-of-the- art methods in handwritten text recognition utilize artificial neural networks to en- code input image data and decode the corresponding text output. However, the architecture of these systems often imposes limitations on either input format or performance. Attention-based neural networks provide alternative alignment techniques that circumvent these restrictions. This thesis proposes a theoretical framework for a handwriting recognition system with a spatial transformer network as an attention mechanism. In order to produce pseudo-ground truth alignment data in a weakly supervised manner, we propose a Viterbi-like loss function that generates a sequence of pixel locations for every trigram in a word transcription through dynamic programming. Although tested on word image data, this framework is extensible to sentence and paragraph text recognition with minor modifications.
©2021 Sophie Milan Li. Access limited to the Smith College community and other researchers while on campus. Smith College community members also may access from off-campus using a Smith College log-in. Other off-campus researchers may request a copy through Interlibrary Loan for personal use.
Li, Sophie Milan, "Attention-based handwritten text recognition with spatial transformer networks" (2021). Honors Project, Smith College, Northampton, MA.
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