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In this paper, we explore and detail our experiments in ahigh-dimensionality, multi-class image classification problem often found inthe automatic recognition of Sign Languages. Here, our efforts are directedtowards comparing the characteristics, advantages and drawbacks of creating andtraining Support Vector Machines disposed in a Directed Acyclic Graph andArtificial Neural Networks to classify signs from the Brazilian Sign Language(LIBRAS). We explore how the different heuristics, hyperparameters andmulti-class decision schemes affect the performance, efficiency and ease of usefor each classifier. We provide hyperparameter surface maps capturing accuracyand efficiency, comparisons between DDAGs and 1-vs-1 SVMs, and effects ofheuristics when training ANNs with Resilient Backpropagation. We reportstatistically significant results using Cohen's Kappa statistic for contingencytables.

Recognizing Static Signs from the Brazilian Sign Language: Comparing  Large-Margin Decision Directed Acyclic Graphs, Voting Support Vector Machines  and Artificial Neural Networks