关键字 |
| 边界，形状，小波变换，特征向量和物体识别。 |
介绍 |
| 近年来，由于纸质文档仍然是信息交换的最主要媒介，因此文档图像分析和光学字符识别(OCR)的荣耀得到了强劲的增长。计算机是处理这些信息的最合适的设备。大部分与OCR相关的工作都是用英语、汉语、日语和阿拉伯语完成的。然而，一些初步的工作也已经完成了印度剧本。在[12]中报道了对手写自动处理技术的全面回顾。这篇论文报告了在这一领域发生的许多最新进展和变化。书写的生成和感知的各种心理物理方面被提出，以突出使书写处理困难的不同来源的可变性。指出了线上和线下方法的主要成功和有前景的应用。[14]报道了亚洲文字的分析与识别。本文综述了近几十年来中日韩三国在手写体识别方面的研究进展。 It presents the recognition methodologies, features explored, databases used and classification schemes. In addition, it includes a description of the performance of numerous recognition systems found in both academic and industrial research laboratories. A handwritten character magically survives serious distortions in size, orientation and even structure. The general problem of defining the shape of a 2D line diagram, with character as a significant special case is addressed in [2]. The paper argues that the global shape of a character is determined by a set of local shapes. The local shapes, which are few in number, combine variously to give rise to a great diversity of characters. An unconstrained handwritten character recognition based on fuzzy logic is described in [5]. The approach uses the box method for feature extraction. Two recognition strategies are implemented for comparison. The recognition based on fuzzy logic outweighs that using back propagation neural network (BPNN). A hybrid classification system with neural network and decision tree as the classifiers for handwritten numeral recognition is reported in [11]. First, a variety of stable and reliable global features are defined and extracted based on the character geometric structures. A novel floating detector is then proposed to detect segments along the left and right profile of a character image used as local features. Finally, the recognition system consists of a hierarchical coarse classification and fine classification. A distance feature for neural network-based recognition of handwritten characters is described in [10]. Two new features, which are based on distance information, one on distance transformation and another on directional distance distribution, are described. Experimentation has been done on three standard distinct sets of characters (i.e., numerals, English capital letters, and Hangul initial sounds). Multiresolution recognition of unconstrained handwritten numerals using wavelet transform and a simple multilayer cluster neural network is reported in [7]. The scheme consists of two stages: a feature extraction stage for extracting multi resolution features with wavelet transform, and a classification stage for classifying unconstrained handwritten numerals with a single multilayer cluster neural network. Work on multi wavelets and neural networks can also be seen in [3]. A MLP Classifier for both printed and handwritten Bangla numeral recognition is proposed in [8]. Pixel-based and shape-based features are chosen for the purpose of recognition. Multi-layer neural network architecture was chosen as classifiers of the mixed class of handwritten and printed numerals. An offline hand printed Bangla numeral recognition scheme using a multistage classifier system comprising of multilayer Perceptron (MLP) neural network is described in [1]. The scheme considers multiresolution features based on wavelet transforms. The recognition scheme is robust to various writing styles and size. Method based on multiresolution analysis for Telugu character recognition can also be seen in [13]. Online handwritten character recognition of Devnagari and Telugu characters using Support Vector Machines is reported in [15]. The input to the recognition system consists of features of the strokes in each written character. The present paper has proposed a method to recognize ten Telugu numerals by using boundary moment invariant descriptors. The proposed method is using Wavelet transform domain for evaluating the feature vector. The organization of the present paper follows as section II gives the methodology, section III gives the results and discussions and section IV gives the conclusions. |
方法 |
| 字符识别是OCR系统中的一项重要工作。本文提出了一种识别泰卢固语0到9数字的方法。输入对象可以通过使用基于边界或区域的表示来表示。本文采用基于边界的表示方法。在所表示的边界形状上，可以描述特征。利用边界矩不变量(BMI)进行特征描述。特征向量的大小为7。BMI1到BMI7由式(1)-(7)给出。 |
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| 本文计算了变换域的BMI特征。利用小波变换实现了变换域。小波是一种数学函数，用于将给定的函数或连续时间信号分解为不同频率的分量，并以与其尺度相匹配的分辨率来研究每个分量。小波变换是用小波表示函数。在小波变换[4,6,9]中，图像信号可以通过将其通过分析滤波器组然后进行抽取操作来分析。该分析滤波器组由每个分解阶段的低通滤波器和高通滤波器组成。当信号通过这些滤波器时，它分裂成两个波段。低通滤波器对应于平均运算，提取信号的粗信息。高通滤波器对应于差分运算，提取信号的详细信息。过滤操作的输出然后被抽取2。 A two-dimensional transform can be accomplished by performing two separate one-dimensional transforms. Firstly, the image is filtered along the xdimension using low pass and high pass analysis filters and decimated by two. Low pass filtered coefficients are stored on the left part of the matrix and high pass filtered coefficients are stored in the right part of the matrix. Because of decimation the total size of the transformed image is same as the original image. Then, it is followed by filtering the subimage along the y-dimension and decimated by two. Finally, the image splits into four bands denoted by low-low (LL), high-low (HL), low-high (LH) and high-high (HH) after one-level decomposition. Fig. 1 shows one level of filtering. This process of filtering the image is called ‘Pyramidal decomposition’ of image. |
| 本文提出了一种利用边界矩不变(BMI)描述符识别0到9的泰卢固语数字的方法。原始图像如图2所示。 |
| 本文采用了基于边界的形状表示方法。利用Haar小波变换将原始图像分解为LL、Hl、LH、HH四个子带，在每个子带内计算7个BMI特征，结果如表I-X所示。这四个子波段的平均值是为每个数字计算的。从图3所示的图表中，可以清楚地看到，泰卢固语数字0的BMI值最高，数字6的BMI值最低。数字1和8的BMI值在较高的范围内。数字7的BMI值处于较低的范围，其余数字BMI值处于中等范围。 |
结论 |
| 字符识别在文档图像分析中起着至关重要的作用。本文提出了一种从0到9识别十个泰卢固语数字的方法。该方法计算了边界形状表示上的7个矩不变特征。特征的计算进一步扩展到通过Haar小波变换得到的四个分解子带。所有这些数值的平均值将为十个泰卢固语数字中的每一个产生一个独特的特征。从结果中，我们可以得出这样的结论:具有最大形状的全/半圆的泰卢固语数字具有最高的特征值，而不具有圆形形状的数字具有最低的特征值，其余具有较少圆形形状的数字具有中等特征值。利用该方法，本文提出了一种识别十个泰卢固语数字的方法。 |
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数字一览 |
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参考文献 |
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菜单
