Evaluating Word String Embeddings and Loss Functions for CNN-Based Word Spotting

Abstract

The recent past has seen CNNs take over the field of word spotting. The dominance of these neural networks is fueled by learning to predict a word string embedding for a given input image. While the PHOC (Pyramidal Histogram of Characters) is most prominently used, other embeddings such as the Discrete Cosine Transform of Words have been used as well. In this work, we investigate the use of different word string embeddings for word spotting. For this, we make use of the recently proposed PHOCNet and modify it to be able to not only learn binary representations. Our extensive evaluation shows that a large number of combinations of word string embeddings and loss functions achieve roughly the same results on different word spotting benchmarks. This leads us to the conclusion that no word string embedding is really superior to another and new embeddings should focus on incorporating more information than only character counts and positions.