{
  "id": "1505.07675",
  "version": "v1",
  "published": "2015-05-28T12:43:22.000Z",
  "updated": "2015-05-28T12:43:22.000Z",
  "title": "Improved Deep Convolutional Neural Network For Online Handwritten Chinese Character Recognition using Domain-Specific Knowledge",
  "authors": [
    "Weixin Yang",
    "Lianwen Jin",
    "Zecheng Xie",
    "Ziyong Feng"
  ],
  "comment": "5 pages, 4 figures, 3 tables. Accepted to appear at ICDAR 2015",
  "categories": [
    "cs.CV"
  ],
  "abstract": "Deep convolutional neural networks (DCNNs) have achieved great success in various computer vision and pattern recognition applications, including those for handwritten Chinese character recognition (HCCR). However, most current DCNN-based HCCR approaches treat the handwritten sample simply as an image bitmap, ignoring some vital domain-specific information that may be useful but that cannot be learnt by traditional networks. In this paper, we propose an enhancement of the DCNN approach to online HCCR by incorporating a variety of domain-specific knowledge, including deformation, non-linear normalization, imaginary strokes, path signature, and 8-directional features. Our contribution is twofold. First, these domain-specific technologies are investigated and integrated with a DCNN to form a composite network to achieve improved performance. Second, the resulting DCNNs with diversity in their domain knowledge are combined using a hybrid serial-parallel (HSP) strategy. Consequently, we achieve a promising accuracy of 97.20% and 96.87% on CASIA-OLHWDB1.0 and CASIA-OLHWDB1.1, respectively, outperforming the best results previously reported in the literature.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2015-05-28T12:43:22.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep convolutional neural network",
      "online handwritten chinese character recognition",
      "domain-specific knowledge",
      "dcnn-based hccr approaches treat"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 5,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2015arXiv150507675Y"
    }
  }
}