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Visualization-Assisted Development of Deep Learning Models in Offline Handwriting Recognition

  • Deep learning is a field of machine learning that has been the focus of active research and successful applications in recent years. Offline handwriting recognition is one of the research fields and applications were deep neural networks have shown high accuracy. Deep learning models and their training pipeline show a large amount of hyper-parameters in their data selection, transformation, network topology and training process that are sometimes interdependent. This increases the overall difficulty and time necessary for building and training a model for a specific data set and task at hand. This work proposes a novel visualization-assisted workflow that guides the model developer through the hyper-parameter search in order to identify relevant parameters and modify them in a meaningful way. This decreases the overall time necessary for building and training a model. The contributions of this work are a workflow for hyper-parameter search in offline handwriting recognition and a heat map based visualization technique for deep neural networks in multi-line offline handwriting recognition. This work applies to offline handwriting recognition, but the general workflow can possibly be adapted to other tasks as well.

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Metadaten
Author:Martin Schall, Dominik Sacha, Manuel Stein, Matthias O. FranzORCiDGND, Daniel KeimGND
URL:https://www.researchgate.net/publication/328687407
Parent Title (English):Visualization in Data Science (VDS at IEEE VIS 2018), 22 October 2018, Berlin, Germany
Document Type:Conference Proceeding
Language:English
Year of Publication:2018
Release Date:2019/01/09
Pagenumber:8
Institutes:Institut für Optische Systeme - IOS
Open Access?:Ja
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)