Journal
INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION
Volume 21, Issue 4, Pages 233-240Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s10032-018-0311-4
Keywords
Handwritten Chinese character recognition; Convolutional neural networks; Cascaded CNN
Categories
Funding
- Beijing Technology Plan Project [Z171100002217094]
- National Defense Science and Technology Project [17-163-12-XJ-003-003-01]
Ask authors/readers for more resources
Deep convolutional neural networks-based methods have brought great breakthrough in image classification, which provides an end-to-end solution for handwritten Chinese character recognition (HCCR) problem through learning discriminative features automatically. Nevertheless, state-of-the-art CNNs appear to incur huge computational cost and require the storage of a large number of parameters especially in fully connected layers, which is difficult to deploy such networks into alternative hardware devices with limited computation capacity. To solve the storage problem, we propose a novel technique called weighted average pooling for reducing the parameters in fully connected layer without loss in accuracy. Besides, we implement a cascaded model in single CNN by adding mid output to complete recognition as early as possible, which reduces average inference time significantly. Experiments are performed on the ICDAR-2013 offline HCCR dataset. It is found that our proposed approach only needs 6.9 ms for classifying a character image on average and achieves the state-of-the-art accuracy of 97.1% while requires only 3.3 MB for storage.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available