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文件名称:Feature Extraction with CNN for Handwritten Word Recognition
文件大小:280KB
文件格式:PDF
更新时间:2018-06-10 04:58:37
DeepLearning
In this paper, we show that learning features with
convolutional neural networks is better than using hand-crafted
features for handwritten word recognition. We consider two kinds
of systems: a grapheme based segmentation and a sliding window
segmentation. In both cases, the combination of a convolutional
neural network with a HMM outperform a state-of-the art HMM
system based on explicit feature extraction. The experiments are
conducted on the Rimes database. The systems obtained with the
two kinds of segmentation are complementary : when they are
combined, they outperform the systems in isolation. The system
based on grapheme segmentation yields lower recognition rate
but is very fast, which is suitable for specific applications such
as document classification.