Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

The recognition of the Arabic characters is a crucial task incomputer vision and Natural Language Processing fields. Some major complicationsin recognizing handwritten texts include distortion and patternvariabilities. So, the feature extraction process is a significant task in NLPmodels. If the features are automatically selected, it might result in theunavailability of adequate data for accurately forecasting the character classes.But, many features usually create difficulties due to high dimensionality issues.Against this background, the current study develops a Sailfish Optimizer withDeep Transfer Learning-Enabled Arabic Handwriting Character Recognition(SFODTL-AHCR) model. The projected SFODTL-AHCR model primarilyfocuses on identifying the handwritten Arabic characters in the inputimage. The proposed SFODTL-AHCR model pre-processes the input imageby following the Histogram Equalization approach to attain this objective.The Inception with ResNet-v2 model examines the pre-processed image toproduce the feature vectors. The Deep Wavelet Neural Network (DWNN)model is utilized to recognize the handwritten Arabic characters. At last,the SFO algorithm is utilized for fine-tuning the parameters involved in theDWNNmodel to attain better performance. The performance of the proposedSFODTL-AHCR model was validated using a series of images. Extensivecomparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of theproposed SFODTL-AHCR model over other approaches.

Wavelet packet based feature extraction and recognition of license plate characters

To study the characteristics of license plate characters recognition,this paper proposes a method for fea- ture extraction of license plate characters based on two-dimensional wavelet packet.We decompose license plate character images with two dimensional-wavelet packet and search for the optimal wavelet packet basis.This paper pre- sents a criterion of searching for the optimal wavelet packet basis,and a practical algorithm.The obtained optimal wavelet packet basis is used as the feature of license plate character,and a BP neural network is used to classify the character.The test- ing results show that the proposed method achieved higher recognition rate than the traditional methods.

Handwritten Numeric and Alphabetic Character Recognition and Signature Verification Using Neural Network

Handwritten signature and character recognition has become challenging research topic due to its numerous applications. In this paper, we proposed a system that has three sub-systems. The three subsystems focus on offline recognition of handwritten English alphabetic characters (uppercase and lowercase), numeric characters (0 - 9) and individual signatures respectively. The system includes several stages like image preprocessing, the post-processing, the segmentation, the detection of the required amount of the character and signature, feature extraction and finally Neural Network recognition. At first, the scanned image is filtered after conversion of the scanned image into a gray image. Then image cropping method is applied to detect the signature. Then an accurate recognition is ensured by post-processing the cropped images. MATLAB has been used to design the system. The subsystems are then tested for several samples and the results are found satisfactory at about 97% success rate. The quality of the image plays a vital role as the images of poor or mediocre quality may lead to unsuccessful recognition and verification.

Handwritten Character Recognition Using Multiresolution Technique and Euclidean Distance Metric

In the present paper, the problem of handwritten character recognition has been tackled with multiresolution technique using discrete wavelet transform (DWT) and Euclidean distance metric (EDM). The technique has been tested and found to be more accurate and faster. Characters is classified into 26 pattern classes based on appropriate properties. Features of the handwritten character images are extracted by DWT used with appropriate level of multiresolution technique, and then each pattern class is characterized by a mean vector. Distances from input pattern vector to all the mean vectors are computed by EDM. Minimum distance determines the class membership of input pattern vector. The proposed method provides good recognition accuracy of 90% for handwritten characters even with fewer samples.

Unsupervised feature selection based on Markov blanket and particle swarm optimization

Feature selection plays an important role in data mining and recognition, especially in the large scale text, image and biological data. Specifically, the class label information is unavailable to guide the selection of minimal feature subset in unsupervised feature selection, which is challenging and interesting. An unsupervised feature selection based on Markov blanket and particle swarm optimization is proposed named as UFSMB-PSO. The proposed method seeks to find the high-quality feature subset through multi-particles' cooperation of particle swarm optimization without using any learning algorithms. Moreover, the features' relevance will be computed based on an information metric of relevance gain, which provides an information theoretical foundation for finding the minimization of the redundancy between features. Our results on several benchmark datasets demonstrate that UFSMB-PSO can achieve significant improvement over state of the art unsupervised methods. © 1990-2011 Beijing Institute of Aerospace Information.

Design and Implementation of Prototype System for Online Handwritten Uyghur Character Recognition

Based on the analysis of the unique shapes and writing styles of Uyghur characters,we design a framework for prototype character recognition system and carry out a systematic theoretical and experimental research on its modules.In the preprocessing procedure,we use the linear and nonlinear normalization based on dot density method.Both structural and statistical features are extracted due to the fact that there are some very similar characters in Uyghur literature.In clustering analysis,we adopt the dynamic clustering algorithm based on the minimum spanning tree（MST）,and use the k-nearest neighbor matching classification as classifier.The testing results of prototype system show that the recognition rates for characters of the four different types（independent,suffix,intermediate,and initial type） are 74.67%,70.42%,63.33%,and 72.02%,respectively;the recognition rates for the case of five candidates for those characters are 94.34%,94.19%,93.15%,and 95.86%,respectively.The ideas and methods used in this paper have some commonality and usefulness for the recognition of other characters that belong to Altaic languages family.

Speech Dictation System Based on Character Recognition

To solve students’ dictation problems, a speech dictation system basedon character recognition is proposed in this paper. The system applied offlinehandwritten Chinese character recognition technology, denoised the imagethrough Gaussian filter, segmented the text through projection method, and convertedthe image to text through OCR technology. The straight line mark in thepicture was detected by Hough transform technology, and then SKB-FSS algorithmand WST algorithm were used for speech synthesis. Experiments show thatthe system can effectively assist students in dictation.

一种基于多维表示的汉字识别方案

针对复杂场景下汉字特征提取难的问题,提出了一种基于多维特征表示的汉字识别方案。首先,提出一种融合空间信息的关键笔形特征提取方法,能够利用少量关键特征实现汉字的唯一识别;其次,通过多任务网络提取多维特征,增强特征提取能力,从而提高汉字识别的准确性;最后,应用字符相似度算法消除噪声,优化识别结果。实验结果表明,相较于可插拔的部首感知分支(PRAB)模型,本方案在场景数据集、网页数据集、文本数据集和手写数据集中的性能分别提升了1.62百分点、1.09百分点、0.15百分点和1.27百分点,证明了该方案的有效性。

面向多复杂场景环境的敞车车号辨识研究

针对现有敞车车号定位识别方法存在的环境适应性差、定位和识别精度低的问题,本文提出一种面向多种复杂环境下的敞车车号精准定位和识别的方法。搭建融合多尺度特征信息的敞车车号定位模型框架,在此基础上,融合多尺度金字塔特征进行深度可分离卷积的敞车车号特征提取网络设计。提出基于改进卷积循环神经网络的车号定位识别模型,主要针对识别网络模型结构进行设计。通过不同环境下采集的敞车车厢图片对本文提出的方法进行验证。结果表明:本文提出的车号定位方法的准确率为0.94,车号识别的准确率为0.97。

在途危化品运输车辆智能识别系统设计与数据提取

危化品车辆抓拍识别是加强危险品运输监管,保障社会公共安全的关键。基于图像处理和极限学习机,提出了一种在途危化品运输车辆抓拍识别系统及算法。对抓拍危化品车辆图像二值化处理,通过模糊算法和形态学膨胀操作提取车牌区域。对提取的区域滤波处理和形态学膨胀操作,提取车牌字符特征。将提取的车牌字符特征作为极限学习机的输入,危化品车辆车牌的33个字母数字作为输出,达到危化品车辆车牌识别的目的。结果表明,所提出的算法对危化品车辆所处环境具有更好的适应性,同时具有更高的抓拍识别精度。这对危化品车辆行驶管理具有一定的实用价值。

基于深度信任网络模型的乌东选煤厂铁路车号图像识别方法

精准识别铁路车号可以为煤厂装车提供依据,从而保证装车环节高效顺利地完成。为此,提出了基于深度信任网络模型的乌东选煤厂铁路车号图像识别方法。首先,利用高速摄像机设备采集原始的车号图像,并利用索贝尔算子检测图像边界;然后,根据列车车号的字体笔画宽度特点,采笔画宽度变换算法定位确定图像中的车号区域,并利用LBP算法提取车号区域内的特征;最后,将提取的特征输入到深度信任网络模型中,在训练网络模型并不断更新参数后,准确识别车号图像。实验表明:该方法能够精准识别乌东选煤厂铁路列车车号图像。在深度信任网络模型中,当受限玻尔兹曼机网络为4层、隐含层节点个数为128个时,该模型的分类识别能力最强,训练损失最小,性能最佳。

一种适用于数显万用表数值提取的方法研究

传统手持式数显万用表通常采用人工检定模式,但存在效率低下、测试数据易人为干预、差错率较高等问题。本文提出一种适用于数显万用表的图像智能识别和数值自动提取方法,实现了测量数据的自动读取、识别和导入,极大地提高了检定工作效率,使计量工作标准化、智能化。

基于YOLOv5与改进VGG-CTC的数字仪表自动读数方法

随着数字电网的发展,数字仪表在电网中的应用越来越广泛。为进一步提高数字仪表自动读数算法的准确率和识别效率,推动数字电网建设,文中提出一种基于YOLOv5和改进VGG-CTC模型的数字仪表自动读数方法。该方法包括数字区域定位和仪表字符识别两部分。首先,采用YOLOv5模型检测输入图像的仪表数字区域,输出预测的数字区域边界框;然后,对VGG网络的卷积层和全连接层参数进行改进,提出一种改进VGG网络,将预测的数字区域输入该网络,即可提取数字区域图像特征,通过长卷积层输出字符概率分布信息;最后,经过CTC转录模块将概率分布序列映射为预测的数字仪表示数。实验结果表明,所提自动读数方法的准确率为89.63%,识别时间为439 ms,相比其他读数方法具有较好的读数准确率和识别速度,能够提高智能电网对设备状态监测的准确性和高效性,对推进能源物联网和数字化电网的建设具有重要意义。

脱机手写体汉字识别综述

脱机手写体汉字识别是模式识别领域最具挑战性的课题之一.本文分析了近年来脱机手写体汉字识别的最新进展,讨论了脱机手写体汉字分割、特征提取和分类器设计等关键技术的各种主流方法,介绍了3种典型的汉字识别数据库,并提出了脱机手写体汉字识别的难点问题和今后发展的趋势,为该领域的研究者指明研究方向,共同促进脱机手写体汉字识别技术的发展.

一种组合特征抽取的新方法

该文提出了一种基于特征级融合的特征抽取新方法 .首先 ,给出了一种合理的特征融合策略 ,即利用复向量给出组合特征的表示 ,将特征空间从实向量空间拓广到复向量空间 .然后 ,发展了具有统计不相关性的鉴别分析的理论 ,并将其用于复向量空间内最优鉴别特征的抽取 .最后 ,在 Concordia大学的 CENPARMI手写体阿拉伯数字数据库以及南京理工大学 NUST6 0 3HW手写汉字库上的试验结果表明 ,所提出的组合特征抽取方法不仅具有很强的维数压缩能力 。

一种基于多特征提取的实用车牌识别方法

针对车牌识别系统的实际应用,利用车牌区域的边缘梯度特征、几何形状特征、颜色特征、灰度纹理特征定位车牌,然后校正车牌图像的颜色及倾斜度;基于灰度投影法,对普通及武警车牌均提出了有效字符分割方案,通过自适应判别去除因字符断裂粘连、特殊字符等造成的干扰;通过基于多特征值提取的神经网络方法初识别车牌;最后将人眼的视觉特性用于模板匹配法,解决易混淆字符及污损车牌的问题。通过大量实验证明,该方法对车牌颜色、拍摄角度、光照条件等限制较少,适用范围广、识别率高,有较强的实用性。

汉字数学表达式的自动生成

汉字的数学表达式是一种全新的汉字表示方法 通过对汉字部件特征的深入分析 ,利用图像处理技术对汉字数学表达式的自动生成做了探讨 选取了大约 5 0 0个基本汉字部件 ,提取了各部件的连通数、亏格数、端点数、折点数、连接点数、交叉点数以及NMI,HNMI ,VNMI值作为汉字部件的基本特征 ;并通过汉字连通区域的分割与合并进行汉字部件的划分和识别 ;最后 ,通过汉字结构的识别得到了汉字的数学表达式 实验中 ,汉字表达式自动生成的正确率为 92 % 这将在排版印刷、广告及包装设计。

一种基于支持向量机的手写汉字识别方法

本文提出了一种新的基于支持向量机手写汉字识别方法 .支持向量机作为一种新的机器学习方法 ,由于其建立在结构风险最小化准则之上 ,而不是仅仅使经验风险达到最小 ,从而使得支持向量分类器具有较好的推广能力 .本文首先讨论了支持向量机的基本原理 ,然后 ,针对支持向量机识别大类别手写汉字所遇到的特殊问题 ,文章进行了分析和阐述 ,并在此基础上 ,提出了基于最小距离分类器预分类的两级分类策略 .最后 ,针对GB2 312 80的 10 34个汉字类别的 12 0套手写样本 ,进行了实验仿真 .实验结果表明 ,本文方法的汉字识别率较距离分类器有较大提高 ,其中多项式核函数的支持向量分类器 ,识别率平均提高 3 38% 。

基于并行特征组合与广义K-L变换的字符识别

针对传统的串行特征融合方法的弱点,提出了一种新的并行特征融合方法.该方法的基本思路是:首先,利用复向量将样本空间上的两组特征集组合起来,构成复特征向量空间;然后,从理论上推广了经典的K-L变换方法与3种基本的K-L展开方法,使其适用于复特征向量空间内的特征抽取.此外,还揭示了并行特征融合的对称性质,并详细讨论了并行特征组合的策略问题.最后,用所提出的方法来解决手写体字符的特征抽取与识别问题.在南京理工大学NUST603HW手写体汉字库以及Concordia大学的CENPARMI手写体阿拉伯数字数据库上的实验结果表明,所提出的特征融合方法不仅较大幅度地提高了识别率,而且识别结果优于传统的串行特征融合方法.

基于RBF网络的光学字符提取与识别新方法

提出了一种新的基于统计与模糊隶属度的光学字符特征提取方法,可以快速准确地识别受噪声污染的光学字符。相比传统算法,本文方法的特征空间区分度更高,最小类间距离扩大33.2%以上。应用在径向基函数(Radical Basis Function,RBF)神经网络中,在字体字号变化且有背景噪声污染的影响下,识别率高达99%以上,且相比直方图投影法提速75%。理论分析与实验结果表明,与传统方法相比,该算法抗噪能力更强、模式区分度更高、时空复杂度更低,更简约、更全面地覆盖了字符的特征,应用范围广。已应用于实际系统,取得很好的实验结果。