In this paper we consider the applicability of similarity metric classifiers for recognition of words. The approaches
based on recognition of single characters are well studied, but they demonstrate poor performance on some kinds of text,
especially the ones hard for segmentation, such as handwritten and Arabic texts, or the ones with ligatures. Moreover,
if images are strongly noised and/or corrupted because of camera imperfection, touched symbols can appear in the text.
All these problems usually occur in recognition systems for text with a predefined pattern, where the set of words is
limited. Given this, it is reasonable to recognize whole words, although the dictionary can be huge and unknown while
training. In this study we suggest using the similarity metric-based neural networks for word images recognition. We
provide the comparison between the similarity metric-based neural network with classifying one on the words collected
from “gender” field of Russian National passport. To maintain the experimental integrity, the parameters of all the
layers except the last one were the same for both types of networks. The results show the relevance of the similarity
metric-based neural networks to word recognition problem solving. The main advantages of the suggested method are the
possibility of network alphabet extension after learning and no need for symbol segmentation.
text recognition, convolutional neural networks, deep learning, siamese neural networks, metrics learning
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