Directional features in online handwriting recognition

The selection of valuable features is crucial in pattern recognition. In this paper we deal with the issue that part of features originate from directional instead of common linear data. Both for directional and linear data a theory for a statistical modeling exists. However, none of these theories gives an integrated solution to problems, where linear and directional variables are to be combined in a single, multivariate probability density function. We describe a general approach for a unified statistical modeling, given the constraint that variances of the circular variables are small. The method is practically evaluated in the context of our online handwriting recognition system frog on hand and the so-called tangent slope angle feature. Recognition results are compared with two alternative modeling approaches. The proposed solution gives significant improvements in recognition accuracy, computational speed and memory requirements.     

Syllables as processing units in handwriting production.

This research focused on the syllable as a processing unit in handwriting. Participants wrote, in uppercase letters, words that had been visually presented. The interletter intervals provide information on the timing of motor production. In Experiment 1, French participants wrote words that shared the initial letters but had different syllable boundaries. In Experiment 2, French- and Spanish-speaking participants wrote cognates and pseudowords with a letter sequence that was always intrasyllabic in French and intersyllabic in Spanish. In Experiment 3, French-Spanish bilinguals wrote the cognates and pseudowords with the same type of sequences. In the 3 experiments, the critical interletter intervals were longer between syllables than within syllables, indicating that word syllable structure constrains motor production both in French and Spanish. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

正线性函数在深度神经网络中的研究

针对深度神经网络中常用的激活函数具有非线性和计算复杂度高的特点,提出使用正线性函数代替常用非线性函数作为深度神经网络的激活函数。基于正线性激活函数建立的深度神经网络模型计算复杂度低,能得到稀疏表示,与人类大脑信息感知具有一致性。通过图像分类任务验证了正线性激活函数在深度神经网络中应用的有效性。     

红外式触摸手写白板应用设计的关键技术

本文给出了红外式触摸手写白板所实现的功能,分析了其中三项关键技术：屏幕校准、模拟用户操作以及笔迹平滑处理;针对各项关键技术,特别是笔迹平滑处理,提出了相应的解决方案,最后验证了其正确性与实用性。     

手写数字识别的研究进展

脱机手写体字符识别技术是当前的热点和难点问题,是解决目前大量已有的文档资料录入工作的关键。在系统控制、人工智能、生物医学工程、遥感数据分析、军事目标识别等领域发挥了重要的作用,在国民经济、国防建设、社会发展和社会治安等方面得到广泛的应用。该文将就手写体字符识别技术的国内外研究进展进行综述。     

Touch‐screen panel integrated into 12.1‐in. a‐Si:H TFT‐LCD

Abstract— A touch‐screen‐panel (TSP) embedded 12.1‐in. LCD employing a standard existing a‐Si:H TFT‐LCD process has been successfully developed. Compared with conventional external touch‐screen panels, which use additional components to detect touch events, the new internal TSP exhibits a clearer image and improved touch feeling, as well as increased sensing speed using discrete sensing lines to enable higher‐speed sensing functions including handwriting. The new internal digital switching TSP can be fabricated with low cost because it does not require any additional process steps compared to a standard a‐Si:H TFT‐LCD.     

Towards a web-based progressive handwriting recognition environment for mathematical problem solving

The emergence of pen-based mobile devices such as PDAs and tablet PCs provides a new way to input mathematical expressions to computer by using handwriting which is much more natural and efficient for entering mathematics. This paper proposes a web-based handwriting mathematics system, called WebMath, for supporting mathematical problem solving. The proposed WebMath system is based on client–server architecture. It comprises four major components: a standard web server, handwriting mathematical expression editor, computation engine and web browser with Ajax-based communicator. The handwriting mathematical expression editor adopts a progressive recognition approach for dynamic recognition of handwritten mathematical expressions. The computation engine supports mathematical functions such as algebraic simplification and factorization, and integration and differentiation. The web browser provides a user-friendly interface for accessing the system using advanced Ajax-based communication. In this paper, we describe the different components of the WebMath system and its performance analysis.     

Off-line recognition of realistic Chinese handwriting using segmentation-free strategy

Great challenges are faced in the off-line recognition of realistic Chinese handwriting. This paper presents a segmentation-free strategy based on Hidden Markov Model (HMM) to handle this problem, where character segmentation stage is avoided prior to recognition. Handwritten textlines are first converted to observation sequence by sliding windows. Then embedded Baum-Welch algorithm is adopted to train character HMMs. Finally, best character string maximizing the a posteriori is located through Viterbi algorithm. Experiments are conducted on the HIT-MW database written by more than 780 writers. The results show the feasibility of such systems and reveal apparent complementary capacities between the segmentation-free systems and the segmentation-based ones.     

基于字素分割的蒙古文手写识别研究

隐马尔科夫模型(HMM)对序列数据有很强的建模能力,在语音和手写识别中都得到了广泛的应用。利用HMM研究蒙古文手写识别,首先需要解决的问题是手写文字的序列化。从蒙古文的构词和书写特点看,蒙古文由多个字素从上到下串联构成。选择字素集合和词的字素分割是手写识别的基础,也是影响识别效果的关键因素。该文根据蒙古文音节和编码知识确定了蒙古文字母集合,共包括1 171个字母。通过相关性处理、HMM排序筛选等手段得到长字素集合,共包括378个字素。对长字素经过人工分解,获得了50个短字素。最后利用两层映射给出了词转字素序列的算法。为了验证长短字素在手写识别中的效果,我们在HTK(hidden Markov model toolkit)环境下利用小规模字库实现了手写识别系统,实验结果表明短字素比长字素有更好的性能。文中给出的字素集合和词转字素序列的算法为后续基于HMM的蒙古文手写识别研究奠定了基础。