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Recognizing text in natural scene images is very important to develop various systems such as an assistant device for visually-impaired people. Multilingual scene text recognition is also becoming important for wearable camera devices with language translation feature. Since computational resources are limited on such mobile devices, fast and accurate Optical Character Recognition (OCR) algorithm is needed. Nearest Neighbor (NN) search is quite popular in feature vector-based OCR systems, and its speed improvement is required. In this paper, we develop an OCR scheme with tree-based clustering technique with LDA (Linear Discriminant Analysis) aiming at real-time Japanese/Chinese character recognition. The experimental results using ETL9B dataset show that our proposed method is 94.6% faster than our previous method, also beating other techniques, at mere 0.24% accuracy drop from the full linear search.

Fast and Accurate Tree-Based Clustering for Japanese/Chinese Character Recognition