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.