Two-tier architecture for unconstrained handwritten character recognition

In this paper, we propose an approach that combines the unsupervised and supervised learning techniques for unconstrained handwritten numeral recognition. This approach uses the Kohonen self-organizing neural network for data classification in the first stage and the learning vector quantization (LVQ) model in the second stage to improve classification accuracy. The combined architecture performs better than the Kohonen self-organizing map alone. In the proposed approach, the collection of centroids at different phases of training plays a vital role in the performance of the recognition system. Four experiments have been conducted and experimental results show that the collection of centroids in the middle of the training gives high performance in terms of speed and accuracy. The systems developed also resolve the confusion between handwritten numerals.     

k-NN classification of handwritten characters via accelerated GAT correlation

This paper addresses the problem of reinforcing the ability of the k-NN classification of handwritten characters via distortion-tolerant template matching techniques with a limited quantity of data. We compare three kinds of matching techniques: the conventional simple correlation, the tangent distance, and the global affine transformation (GAT) correlation. Although the k-NN classification method is straightforward and powerful, it consumes a lot of time. Therefore, to reduce the computational cost of matching in k-NN classification, we propose accelerating the GAT correlation method by reformulating its computational model and adopting efficient lookup tables. Recognition experiments performed on the IPTP CDROM1B handwritten numerical database show that the matching techniques of the simple correlation, the tangent distance, and the accelerated GAT correlation achieved recognition rates of 97.07%, 97.50%, and 98.70%, respectively. The computation time ratios of the tangent distance and the accelerated GAT correlation to the simple correlation are 26.3 and 36.5 to 1.0, respectively.     

基于BP神经网络的手写数字识别的算法

由于BP神经网络具有并行处理信息、自组织、自学习信息等优点,本文采用了BP神经网络对手写数字识别进行运算,提取笔画密度、长宽比和欧拉数等特征作为训练样本.并用Matlab对其算法进行仿真,并且很准确的识别出来,说明其有非常广泛的前景.     

数字识别算法研究

对数字识别算法进行了深入的研究,提出了切割法,并且在此基础上进行了相应的改进,进而介绍了边缘跟踪法。为了提高跟踪的效率,在边缘跟踪法的跟踪过程中引进了迷宫算法。通过建立了相应的算法模型,分析了各自算法对数字识别的影响。实验结果表明,边缘跟踪法对数字的识别率可以达到95%左右,而切割法对数字的识别率则只能达到70%左右。因此边缘跟踪法比切割法在数字识别中存在着精度高、准确性好等优点。     

基于iPhone手机的数字码实时识别与应用

根据苹果手机拍摄防伪标签数字实时识别的需要,针对防伪数字字号较小的因素和苹果手机因拍摄距离的原因造成的图像缩小、数字模糊、背景复杂等问题进行处理,提高识别精度。首先通过人工选取数字码区域,并进行背景数字分离,定位获取数字图像;其次采用灰度化和二值化得到黑白图像;然后通过投影对数字码图像进行分割,并对每个数字图像进行归一化、锐化和细化;基于统计学抽取数字码的特征,采用最近邻域判别函数进行数字码识别,取得很好的识别效果。     

基于BP人工神经网络的手写体数字识别

手写体数字识别是模式识别研究领域多年来的热点,BP人工神经网络是目前应用最广泛的神经网络模型之一。将两者融合并结合Matlab软件,提出了一种简单的基于BP神经网络数字识别的方法,仿真实验结果表明,该方法识别效果良好,准确率高,有一定实用性。     

基于模板匹配的手写数字决策分类器设计

针对手写数字识别提出一种基于模板匹配决策分类器设计方法。就该方法下的模式识别分类器设计进行详细论述,给出该分类器算法实现。该算法在对手写的数字图像进行预处理的基础上从待识别的手写数字图像中提取若干特征量与事先建立的标准模板库中模板对应的特征量进行比较,计算待识别图像和标准模板特征量之间的距离,用最小距离法判定其所属类。实验结果表明,该决策分类器算法实现容易,匹配速度快,保证字符识别的正确率。     

科技写作中数字表达的探讨

文章分析总结了石油科技期刊的数字表达方式,与《关于出版物上数字用法的试行规定》进行对比,指出了目前数字使用中存在的不合理之处,并对数字分节、参数及偏差范围、约数的使用等几个方面提出了几点建议。     

Recognition of handwritten Chinese characters by critical region analysis

The problem of recognizing offline handwritten Chinese characters has been investigated extensively. One difficulty is due to the existence of characters with very similar shapes. In this paper, we propose a “critical region analysis” technique which highlights the critical regions that distinguish one character from another similar character. The critical regions are identified automatically based on the output of the Fisher's discriminant. Additional features are extracted from these regions and contribute to the recognition process. By incorporating this technique into the character recognition system, a record high recognition rate of 99.53% on the ETL-9B database is obtained.     

Embedding HMMs-based models in a Euclidean space: the topological hidden Markov models

Current extensions of hidden Markov models such as structural, hierarchical, coupled, and others have the power to classify complex and highly organized patterns. However, one of their major limitations is the inability to cope with topology: When applied to a visible observation (VO) sequence, the traditional HMM-based techniques have difficulty predicting the n-dimensional shape formed by the symbols of the VO sequence. To fulfill this need, we propose a novel paradigm named “topological hidden Markov models” (THMMs) that classifies VO sequences by embedding the nodes of an HMM state transition graph in a Euclidean space. This is achieved by modeling the noise embedded in the shape generated by the VO sequence. We cover the first and second level topological HMMs. We describe five basic problems that are assigned to a second level topological hidden Markov model: (1) sequence probability evaluation, (2) statistical decoding, (3) structural decoding, (4) topological decoding, and (5) learning. To show the significance of this research, we have applied the concept of THMMs to: (i) predict the ASCII class assigned to a handwritten numeral, and (ii) map protein primary structures to their 3D folds. The results show that the second level THMMs outperform the SHMMs and the multi-class SVM classifiers significantly.