AN ADAPTIVELY TRAINED KERNEL-BASED NONLINEAR REPRESENTOR FOR HANDWRITTEN DIGIT CLASSIFICATION

In practice, retraining a trained classifier is necessary when novel data become available. This paper adopts an incremental learning procedure to adaptively train a Kernel-based Nonlinear Representor (KNR), a recently presented nonlinear classifier for optimal pattern representation, so that its generalization ability may be evaluated in time-variant situation and a sparser representation is obtained for computationally intensive tasks. The addressed techniques are applied to handwritten digit classification to illustrate the feasibility for pattern recognition.

Handwritten digit recognition based on ghost imaging with deep learning

We present a ghost handwritten digit recognition method for the unknown handwritten digits based on ghost imaging(GI)with deep neural network,where a few detection signals from the bucket detector,generated by the cosine transform speckle,are used as the characteristic information and the input of the designed deep neural network(DNN),and the output of the DNN is the classification.The results show that the proposed scheme has a higher recognition accuracy(as high as 98%for the simulations,and 91%for the experiments)with a smaller sampling ratio(say 12.76%).With the increase of the sampling ratio,the recognition accuracy is enhanced.Compared with the traditional recognition scheme using the same DNN structure,the proposed scheme has slightly better performance with a lower complexity and non-locality property.The proposed scheme provides a promising way for remote sensing.

MCS HOG Features and SVM Based Handwritten Digit Recognition System

Digit Recognition is an essential element of the process of scanning and converting documents into electronic format. In this work, a new Multiple-Cell Size (MCS) approach is being proposed for utilizing Histogram of Oriented Gradient (HOG) features and a Support Vector Machine (SVM) based classifier for efficient classification of Handwritten Digits. The HOG based technique is sensitive to the cell size selection used in the relevant feature extraction computations. Hence a new MCS approach has been used to perform HOG analysis and compute the HOG features. The system has been tested on the Benchmark MNIST Digit Database of handwritten digits and a classification accuracy of 99.36% has been achieved using an Independent Test set strategy. A Cross-Validation analysis of the classification system has also been performed using the 10-Fold Cross-Validation strategy and a 10-Fold classification accuracy of 99.26% has been obtained. The classification performance of the proposed system is superior to existing techniques using complex procedures since it has achieved at par or better results using simple operations in both the Feature Space and in the Classifier Space. The plots of the system’s Confusion Matrix and the Receiver Operating Characteristics (ROC) show evidence of the superior performance of the proposed new MCS HOG and SVM based digit classification system.

Recognition of Handwritten Words from Digital Writing Pad Using MMU-SNet

In this paper,Modified Multi-scale Segmentation Network(MMU-SNet)method is proposed for Tamil text recognition.Handwritten texts from digi-tal writing pad notes are used for text recognition.Handwritten words recognition for texts written from digital writing pad through text file conversion are challen-ging due to stylus pressure,writing on glass frictionless surfaces,and being less skilled in short writing,alphabet size,style,carved symbols,and orientation angle variations.Stylus pressure on the pad changes the words in the Tamil language alphabet because the Tamil alphabets have a smaller number of lines,angles,curves,and bends.The small change in dots,curves,and bends in the Tamil alphabet leads to error in recognition and changes the meaning of the words because of wrong alphabet conversion.However,handwritten English word recognition and conversion of text files from a digital writing pad are performed through various algorithms such as Support Vector Machine(SVM),Kohonen Neural Network(KNN),and Convolutional Neural Network(CNN)for offline and online alphabet recognition.The proposed algorithms are compared with above algorithms for Tamil word recognition.The proposed MMU-SNet method has achieved good accuracy in predicting text,about 96.8%compared to other traditional CNN algorithms.

Part-based methods for handwritten digit recognition

In this paper, we intensively study the behavior of three part-based methods for handwritten digit recognition. The principle of the proposed methods is to represent a handwritten digit image as a set of parts and recognize the image by aggregating the recognition results of individual parts. Since part-based methods do not rely on the global structure of a character, they are expected to be more robust against various delormations which may damage the global structure. The proposed three methods are based on the same principle but different in their details, for example, the way of aggregating the individual results. Thus, those methods have different performances. Experimental results show that even the simplest part-based method can achieve recognition rate as high as 98.42% while the improved one achieved 99.15%, which is comparable or even higher than some state-of-the-art method. This result is important because it reveals that characters can be recognized without their global structure. The results also show that the part-based method has robustness against deformations which usually appear in handwriting.

基于生成对抗网络的手写数字重叠图像分离与识别

为解决手写数字重叠给识别带来的障碍,探索识别重叠手写数字的高效方法,本文提出一种采用生成对抗网络分离重叠手写数字的方法,将重叠手写数字分离成2个单独的数字后再进行识别。分别使用卷积层和反卷积层构建判别器和生成器,充分提取数字特征,减少模型参数量;融入自注意力机制,快速有效提取数字显著区域特征;对生成器和判别器进行谱归一,约束网络梯度;改进模型损失函数,提高生成器分离数字的质量。在通过MNIST数据集构造的数据上进行试验,结果表明:本文提出的方法对重叠手写数字的识别准确率达95.91%;峰值信噪比和结构相似性指数分别为22.11和0.8961,相比CapsNet网络模型有了显著提升。

基于深度学习的CNN手写体数字识别

从深度学习卷积神经网络(CNN)的结构功能出发,重点研究了手写体数字通过神经网络的识别性能,以及当手写体数字染有各种噪声(高斯噪声、椒盐噪声、泊松噪声和斑点噪声等)时的性能。为了改善和提高卷积神经网络的性能,采用迁移学习技术,将ReLu学习单元更换为LeakyReLu学习单元,其他层保持不变,其目的是为了进一步改善Sigmod神经元函数易饱和的缺点,提高了学习效率和速度。

基于卷积神经网络的手写数字识别技术研究

手写数字识别与我们的生活和工作息息相关,传统的人工判断手写数字方式需要耗费大量精力,并且存在准确性不高和时效性不能保证的弊端。为解决这一问题,采用卷积神经网络方法来进行手写数字识别。首先将MNIST数据集划分为训练集与测试集,其次在MATLAB上搭建训练LeNet-5与AlexNet两种卷积神经网络模型,之后将测试集导入模型,测试性能,最后再将验证集导入验证模型性能。实验结果表明,AlexNet模型对验证集识别率高于LeNet-5模型,且AlexNet模型的稳定性优于LeNet-5。

基于蜂鸟 E203 RISC-V 处理器的手写数字识别系统设计

手写数字识别是计算机视觉领域的一个经典问题,在车牌识别、光学字符识别等领域有重要作用。在嵌入式设备中部署高性能的手写数字识别系统,由于受到ARM和X86架构的约束,其系统的算力、成本、功耗等指标均不理想。RISC-V架构具有开源、精简、扩展性强和指令编码规整等优势,近年在业内备受好评。对开源的蜂鸟E203 RISC-V处理器进行优化,并加入卷积神经网络协处理器单元完成对手写数字的识别。测试结果表明,在系统工作频率为25 MHz时,采用蜂鸟E203 RISC-V处理器设计的卷积神经网络协处理器在进行手写数字识别时,平均识别耗时1 ms,处理视频流数据平均帧数在912帧,正确率为98%,证实了本系统的可行性,体现了RISC-V对比ARM以及X86架构处理器的优越性。

贝叶斯分类器在手写数字分类识别中的应用

手写体数字识别技术是一种非常重要的技术,将带来巨大的社会效益和经济效益。但是到目前为止,对手写数字进行识别的准确率仍需要进一步提高。针对这种情况,本文提出了一种基于贝叶斯分类器的手写体数字分类识别方法,能够有效识别书写规范的数字。

基于PCA降维的MNIST手写数字识别优化

PCA数据降维技术广泛应用于数据降维和数据的特征提取,可以很大程度上降低算法的计算复杂度,提升程序运行效率。文章将MNIST原始数据集和对原始数据集进行PCA降维处理之后的数据集作为样本,分别采用K-邻近算法、决策树ID3算法、SVC分类模型,以及选取不同分类算法作为基础分类器的集成学习方法,实现手写数字识别。在对MNIST数据集进行PCA降维前后,以及不同分类算法和模型执行结果的时间复杂度与预测准确率进行比对与分析,进一步强化与优化手写数字识别准确率等各项指标。

Constructing an AI Compiler for ARM Cortex-M Devices

The diversity of software and hardware forces programmers to spend a great deal of time optimizing their source code,which often requires specific treatment for each platform.The problem becomes critical on embedded devices,where computational and memory resources are strictly constrained.Compilers play an essential role in deploying source code on a target device through the backend.In this work,a novel backend for the Open Neural Network Compiler(ONNC)is proposed,which exploits machine learning to optimize code for the ARM Cortex-M device.The backend requires minimal changes to Open Neural Network Exchange(ONNX)models.Several novel optimization techniques are also incorporated in the backend,such as quantizing the ONNX model’s weight and automatically tuning the dimensions of operators in computations.The performance of the proposed framework is evaluated for two applications:handwritten digit recognition on the Modified National Institute of Standards and Technology(MNIST)dataset and model,and image classification on the Canadian Institute For Advanced Research and 10(CIFAR-10)dataset with the AlexNet-Light model.The system achieves 98.90%and 90.55%accuracy for handwritten digit recognition and image classification,respectively.Furthermore,the proposed architecture is significantly more lightweight than other state-of-theart models in terms of both computation time and generated source code complexity.From the system perspective,this work provides a novel approach to deploying direct computations from the available ONNX models to target devices by optimizing compilers while maintaining high efficiency in accuracy performance.

基于手写体数字识别的激活函数对比研究

针对手写体数据集在卷积神经网络训练时分别使用sigmoid、tanh、relu与mish激活函数进行实验,对这四种激活函数下训练集误差值收敛曲线以及测试集精度曲线进行算法结果分析,实验结果表明,mish激活函数在手写体数据集上进行卷积神经网络训练时效果更佳,而同样得出不同的训练数据集、激活函数的选择会影响算法对数据的拟合度和神经网络的收敛性。

基于CNN网络的手写体数字识别系统的实现

手写体数字识别现在仍是图像识别分类的一个热点,而基于卷积神经网络的深度学习算法具有局部区域连接、权值共享、降采样的结构特点,使得卷积神经网络在图像处理领域有出色表现。以实现手写体数字高精度识别为目标,设计并实现一个基于卷积神经网络的高精度手写体数字识别系统。首先,通过Pyqt5平台设计一个人机交互的GUI界面,其次进行手写体数字图像的采集与预处理,变换成规范的三维向量输入到CNN网络卷积层中,接着进行各个网络层的运算处理,最后通过Softmax输出分类结果。仿真实验结果下MNIST数据集识别模式下的识别率为99.9%,手写输入识别模式下的识别率为98%。结果表明:基于CNN的神经网络识别准确率高,实现技术简单,实用性高。

基于人工智能的手写数字识别方法研究

手写数字识别属于图像分类问题。因个体手写数字的差异,传统的图像分类方法实现快速有效识别的难度相对较大。随着人工智能和计算机硬件技术的快速发展,基于深度学习卷积网络的手写数字识别逐渐成为研究热点。使用PyTorch搭建了经典的网络模型LeNet-5和改进的ResNet18模型进行手写数字识别。采用交叉熵损失函数和Adam优化算法,并设置学习率为0.001,在MNIST数据集上进行了训练和测试,鉴于ResNet18比LeNet-5网络结构深,在训练时花费的时间比LeNet-5多。经过100个Epoch后,使用LeNet-5模型在测试集上准确率达到了99.18%,使用ResNet18卷积模型的准确率高达99.55%,可以识别自制的手写数字,为人工智能识别系统的发展提供了一定的参考价值。

基于STM32的手写数字识别平台的设计与实现

设计了基于STM32的手写数字识别平台,首先,利用MNIST数据集去训练和测试BP神经网络的权重和偏重,在得到理想的训练结果后结束训练和测试;其次,利用C语言编写识别模型并移植至STM32单片机硬件平台;最后,利用STM32单片机硬件平台上的可触摸屏采集实时书写的0~9中任意数字进行实验,利用串口打印识别结果并进行统计,实验结果表明,该平台能够准确识别手写数字。

基于TensorFlow的LeNet网络的手写数字识别与实现

手写数字识别是模式识别中非常重要和关键的研究领域,并且具有一定的难度。每个人的手写习惯各不相同,而且手写数字识别很难建立精确的数学模型,使得手写数字的识别难度大大增加。针对手写数字难以准确识别的问题,将LeNet神经网络应用到其中,并对LeNet模型的输入层、卷积层、池化层及全连接层进行原理方面的剖析。基于TensorFlow的框架和Python语言进行了相关的程序编写与设计,以MNIST库作为数据集的来源,使用PyQt5设计GUI画板界面,最终实现对于手写数字的识别。实验结果表明,本系统的识别准确度和辨识度达到99.2%,可应用于发票手写数字识别、酒店登记手写身份证识别等生活实践中。

基于神经网络的快速识别手写数字研究

针对神经网络模型中结构层次复杂、模型参数过多、手写数字识别时间长等问题,提出了一种快速识别手写数字的方法.之后结合传统人工神经网络和卷积神经网络的优点,利用主成分分析方法对数据集进行降维,重构了一种可以缩短手写数字识别时间的神经网络模型BP-CNN.经过测试BP-CNN在MNIST手写数据集的应用中,能够稳定缩短40%以上的模型训练时间.随着数据集或训练迭代次数的增加,重构神经网络提高手写数字识别效率也随之提高.

基于Keras和卷积神经网络CNN的手写数字识别研究

科技及信息化的应用在生活中随处可见,但也会带来一些问题,如手写试卷的文字识别精准度太低等。文章在深度学习中利用卷积神经网络完成手写数字识别模型的训练。为了构建出合理有效的模型结构,需要使用卷积网络在卷积层中安排适量的卷积核(也叫过滤器),并结合训练的数据得以让模型学习反映不同的手写数字卷积核心的10个卷积权重,最后通过全连接层使用softmax函数给出与每个数字的概率相对应的数字地图的预测概率。卷积神经网络的应用是将手写数字图像转化为数字标签,在MINST数据集上实现数据识别。首先,对数据进行预处理,要对WNIST数据集中将近60000个训练数据和10000个测试数据进行分析计算。数据的组成结构分为图像(数字图像)和标签.(实数)两部分。用6000*28x28x的二维矩阵形式把数字识别特征值演示出来。其次,利用深度学习框架Keras的特性训练MINST数据集,最终生成高精度识别模型,可以使手写数字的识别精准度达到99%,识别效果和速率大大提高。

基于全连接层卷积神经网络识别手写数字的研究

手写体识别信息程序直接运用计算机进行识别录入可以提高对于手写体数字的录入效率。目前,手写体数字的识别在MNIST这种大型标准数据集上的研究是人工智能领域的基础研究。该系统是基于TensorFlow的手写识别系统,TensorFlow机器学习框架技术已经逐渐成熟,对于目前人工智能发展的浪潮,人们越来越热衷于将一些以前机器不能实现的、难以理解的工作通过机器学习的方式来实现。该系统研究的是对于手写体数字的识别,采用TensorFlow框架以及机器学习的方法,详细讨论如何建立全连接层以及对数据模型进行优化,并使用数据集对计算机进行训练,形成参数文件,详细通过对手写体数字形成图像文件进行识别并输出识别数字。总体来说,手写体数字识别是对于手写体信息的一次创新尝试。