Comparison of smoothing techniques and recognition methods for online Kannada character recognition system

机译：在线Kannada字符识别系统的平滑技术和识别方法的比较

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This paper aimed at working on Online Recognition of Handwritten Kannada Characters. The recognition was done for the Top, Middle and Bottom strokes of Kannada characters. Genius MousePen i608X was used to collect the handwritten character samples to build the database. Handwritten character samples were collected for each character from a particular target-group which includes people who are native to Kannada language and belong to different age groups. These samples were semi-automatically validated, pre-processed and features were extracted. Segmentation of characters was done to divide the strokes into top stroke, middle stroke and bottom stroke. These segmented strokes were individually processed. The pre-processing techniques used in the project include removal of duplicated points, smoothing, interpolating missing points, resampling of points and size normalization. Smoothing techniques was compared for Gaussian and Moving Average Smoothing. Dominant point, writing direction and the curvature features were also extracted. In addition to this, recognition was carried out by KNN and SVM pattern recognition methods and a second level of verification rules was incorporated, yielding a maximum recognition rate of 92.5% for KNN and 94.35% for SVM.

机译：本文旨在研究手写卡纳达语字符的在线识别。卡纳达语字符的上，中和下笔画已被认可。 Genius MousePen i608X用于收集手写字符样本以建立数据库。从特定的目标人群中为每个字符收集手写的字符样本，这些目标人群包括卡纳达语族和不同年龄组的人。这些样品经过半自动验证，预处理并提取了特征。进行字符分割以将笔划分为上笔画，中笔画和下笔画。这些分段的笔画分别进行处理。该项目中使用的预处理技术包括删除重复的点，进行平滑，对缺失的点进行插值，对点进行重采样和尺寸归一化。比较了高斯和移动平均平滑的平滑技术。还提取了主要点，书写方向和曲率特征。除此之外，通过KNN和SVM模式识别方法进行识别，并加入第二级验证规则，从而对KNN和SVM的最大识别率分别为92.5％和94.35％。

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来源
《2014 International Conference on Advances in Engineering and Technology Research》|2014年|1-5|共5页
会议地点 Unnao (IN)
作者
Shwetha D.; Ramya S.;
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作者单位

Dept. of Electron. Commun, Manipal Inst. of Technol. (MIT), Manipal, India;

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原文格式 PDF
正文语种 eng
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关键词
Gaussian processes; feature extraction; handwritten character recognition; image segmentation; interpolation; learning (artificial intelligence);

机译：高斯过程;特征提取;手写字符识别;图像分割;插值;学习（人工智能）;

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1. Modified region decomposition method and optimal depth decision tree in the recognition of non-uniform sized characters - An experimentation with Kannada characters [J] . P.Nagabhushan, Radhika M.pai Pattern Recognition Letters . 1999,第14期

机译：识别非均等大小字符的改进区域分解方法和最佳深度决策树-Kannada字符实验
2. Fusion of Complementary Online and Offline Strategies for Recognition of Handwritten Kannada Characters [J] . Rakesh Rampalli, Angarai Ganesan Ramakrishnan Journal of Universal Computer Science . 2011,第1期

机译：互补的在线和离线策略融合，用于手写卡纳达语字符识别
3. Fusion of Complementary Online and Offline Strategies for Recognition of Handwritten Kannada Characters [J] . Rakesh Rampalli, Angarai Ganesan Ramakrishnan Journal of Universal Computer Science . 2011,第1期

机译：互补的在线和离线策略融合，用于手写卡纳达语字符识别
4. Comparison of smoothing techniques and recognition methods for online Kannada character recognition system [C] . Shwetha D., Ramya S. International Conference on Advances in Engineering and Technology Research . 2014

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7. Kannada Characters and Numerical Recognition System using Hybrid Zone-Wise Feature Extraction and Fused Classifier [O] . 2016

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Comparison of smoothing techniques and recognition methods for online Kannada character recognition system

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