Combination of global and local contexts for text/non-text classification in heterogeneous online handwritten documents
Combination of global and local contexts for text/non-text classification in heterogeneous online handwritten documents
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DOI：
10.1016/j.patcog.2015.07.012
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发表时间：
2016-03
期刊：
影响因子：
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通讯作者：
T. V. Phan;M. Nakagawa
中科院分区：
文献类型：
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作者：
T. V. Phan;M. Nakagawa
在线手写文档中的文本/非文本分类任务对于文本识别、文本搜索和图表解释至关重要。然而，由于大量的变异和缺乏先验知识，这是一个具有挑战性的问题。为了解决这一问题，我们提出了利用全局上下文和局部上下文来构建高性能的分类器。该分类器将文本或非文本标签分配给数字墨迹文档的笔划序列中的每个笔划。首先，使用神经网络结构来获取笔画序列的完整全局上下文。然后，为了改善序列标注结果，对相邻笔画的局部时间上下文使用了一种简单而有效的基于边缘分布的模型。在现有的异质在线手写文档库上的实验结果证明了该上下文组合方法的优越性和有效性。该方法在Kondate(日文)和IAMonDo(英文)文档数据库上的分类正确率分别达到99.04%和98.30%。这些结果明显好于文献中报道的其他结果。
The task of text/non-text classification in online handwritten documents is crucially important to text recognition, text search, and diagram interpretation. It, however, is a challenging problem because of the large amount of variation and lack of prior knowledge. In order to solve this problem, we propose to use global and local contexts to build a high-performance classifier. The classifier assigns a text or non-text label to each stroke in a stroke sequence of a digital ink document. First, a neural network architecture is used to acquire the complete global context of the sequence of strokes. Then, a simple but effective model based on a marginal distribution is used for the local temporal context of adjacent strokes in order to improve the sequence labeling result. The results of experiments on available heterogeneous online handwritten document databases demonstrate the superiority and effectiveness of our context combination approach. Our method achieved classification rates of 99.04% and 98.30% on the Kondate (written in Japanese) and IAMonDo (written in English) heterogeneous document databases. These results are significantly better than others reported in the literature.