用于手写化学表达式识别的在线化学符号识别

Peng Tang, S. Hui, Chi-Wing Fu
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引用次数: 12

摘要

随着苹果的iPad和三星的Galaxy Tablet等手写和触控设备的日益普及,手写已经成为一种重要的输入法。虽然这些设备很好地支持对文本内容和数学公式的手写识别,但由于其复杂的空间结构,手写化学表达式的识别仍然非常具有挑战性。在本研究中,我们重点研究了化学符号识别,这是准确识别手写化学表达式的关键。特别地,我们提出了一种用于手写化学符号识别的在线混合支持向量机-弹性匹配(SVM-EM)方法。基于所提出的化学符号识别方法和在线结构分析,我们在苹果iOS平台上实现了一个在线手写化学表达式识别系统。在本文中,我们提出了一种用于手写化学符号识别的SVM-EM方法,并与几个用户进行了评估,以验证其作为实际应用的良好性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Online chemical symbol recognition for handwritten chemical expression recognition
With the growing popularity of pen-based and touch-based devices such as Apple's iPad and Samsung's Galaxy Tablet, handwriting has become an important input method. Although handwriting recognition for text contents and mathematical formulae are well-supported in these devices, recognizing handwritten chemical expressions is still very challenging due to its complex spatial structure. In this research, we focus on chemical symbol recognition which is essential for accurate handwritten chemical expression recognition. In particular, we propose an online hybrid Support Vector Machine - Elastic Matching (SVM-EM) approach for handwritten chemical symbol recognition. Based on the proposed chemical symbol recognition approach and an online structural analysis, we have implemented an online handwritten chemical expression recognition system on Apple's iOS platform. In this paper, we present our proposed SVM-EM approach for handwritten chemical symbol recognition and evaluate it with several users to verify its promising performance as a real application.
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