### 机器学习代写|深度学习project代写deep learning代考|DEEP LEARNING BASED APPROACHES FOR TEXT RECOGNITION

statistics-lab™ 为您的留学生涯保驾护航 在代写深度学习deep learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写深度学习deep learning代写方面经验极为丰富，各种代写深度学习deep learning相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 机器学习代写|深度学习project代写deep learning代考|HyShalini Agrahari* and Arvind Kumar Tiwari

Text recognition has risen in popularity in the area of computer vision and natural language processing due to its use in different fields. For character recognition in a handwriting recognition system, several methods have been suggested. There are enough studies that define the techniques for translating text information from a piece of paper to an electronic format. Text recognition systems may play a key role in creating a paper-free environment in the future by digitizing and handling existing paper records. This chapter provides a thorough analysis of the field of Text Recognition.

We are all familiar with the convenience of having an editable text document that can be easily read by a computer and the information can be used for a variety of uses. People always wanted to use the text that is present in various forms all around them, such as handwritten documents, receipts, images, signboards, hoardings, street signs, nameplates, number plates of automobiles, as subtitles in videos, as captions for photos, and in a variety of other ways. However, we are unable to make use of this information because our computer is unable to recognize these texts purely based on their raw images. Hence, researchers around the world have been trying hard to make computers worthy of directly recognizing text by acquiring images to use the several information sources that could be used in a variety of ways by our computers. In most cases, we have no choice but to typewrite handwritten information, which is very timeconsuming. So, here we have a text recognition system that overcomes these problems. We can see the importance of a ‘Text Recognition System’ just by having to look at these scenarios, which have a wide range of applications in security, robotics, official documentation, content filtering, and many more.

Due to digitalization, there is a huge demand for storing data into the computer by converting documents into digital format. It is difficult to recognize text in various sources like text documents, images, and videos, etc. due to some noise. The text recognition system is a technique by which recognizer recognizes the characters or texts or various symbols. The text recognition system consists of a procedure of transforming input images into machine-understandable format $[1,2]$.

The use of text recognition has a lot of benefits. For example, we find a lot of historical papers in offices and other places that can be easily replaced with editable text and archived instead of taking up too much space with their hard copies. Online and offline text recognition are the two main types of recognition whether online recognition system includes tablet and digital pen, while offline recognition includes printed or handwritten documents [3].

## 机器学习代写|深度学习project代写deep learning代考|Convolutional Neural Network

CNN [6] is a method of deep learning algorithm that is specifically trained to perform with image files. A simple class that perfectly represents the image in CNN, processed through a series of convolutional layers, a pooling layer, and fully connected layers. CNN can learn multiple layers of feature representations of an image by applying

different techniques. Low-level features such as edges and curves are examined by image classification in this method and a sequence of convolutional layers helps in building up to more abstract. CNN provides greater precision and improves performance because of its exclusive characteristics, such as local connectivity and parameter sharing. The input layer, multiple hidden layers (convolutional, normalization, pooling), and a fully connected and output layer make up the system of CNN. Neurons in one layer communicate with some neurons in the next layer, making the scaling simpler for higher resolutions.

In the input layer, the input file is recorded and collected. This layer contains information about the input image’s height, width, and several channels (RGB information). To recognize the features, the network will use a sequence of convolutions and pooling operations in multiple hidden layers. Convolution is one of the most important components of a CNN. The numerical mixture of multiple functions to produce a new function is known as convolution. Convolution is applied to the input image via a filter or, to produce a feature map in the case of a CNN. The input layer contains $n \times n$ input neurons which are convoluted with the filter size of $m$ $\times m$ and return output size of $(n-m+1) \times(n-m+1)$. On our input, we perform several convolutions, each with a different filter. As a result, different feature maps emerge. Finally, we combine these entire feature maps to create the convolution layer final output. To reduce the input feature space and hence reduces the higher computation; a pooling layer is placed between two convolutional layers. Pooling allows passing only the values you want to the next layer, leaving the unnecessary behind. This reduces training time, prevents overfitting, and helps in feature selection. The max-pooling operation takes the highest value from each sub-region of the image vector while keeping the most information, this operation is generally preferred in modern applications. CNN’s architecture, like regular neural network architecture, includes an activation function to present non-linearity into the system. Among the various activation functions used extensively in deep learning models, the sigmoid function rectified linear unit (ReLu), and softmax are some wellknown examples. In CNN architecture, the classification layer is the final

layer. It’s a fully connected feed-forward network that’s most commonly used as a classifier. This layer determines predicted classes by categorizing the input image, which is accomplished by combining all the previous layers’ features.

Image recognition, image classification, object detection, and face recognition are just a few of the applications for CNN. The most important section in CNN is the feature extraction section and classification section.

## 机器学习代写|深度学习project代写deep learning代考|Recurrent Neural Network

RNN is a deep learning technique that is both effective and robust, and it is one of the most promising methods currently in use because it is the only one with internal storage. RNN is useful when it is required to predict the next word of sequence [7]. When dealing with sequential data (financial data or the DNA sequence), recurrent neural networks are commonly used. The reason for this is that the model employs layers, which provide a short-term memory for the model. Using this memory, it can more accurately determine the next data and memorize all the information about what was calculated. If we want to use sequence matches in such data, we’ll need a network with previous knowledge of the data. The output from the previous step is fed into the current step in this approach. The architecture of RNN includes three layers: input layer, hidden layer, and output layer. The hidden layer remembers information about sequences.

If compare RNN with a traditional feed-forward neural network(FNN), FNN cannot remember the sequence of data. Suppose we give a word “hello” as input to FNN, FNN processes it character by character. It has already forgotten about ‘ $h$ ‘ ‘e’ and ‘ $l$ ‘ by the time it gets to the character ‘ $o$ ‘. Fortunately, because of its internal memory, a recurrent neural network can remember those characters. This is important because the data sequence comprises important information about what will happen next, that’s why an RNN can perform tasks that some other techniques cannot.

## 机器学习代写|深度学习project代写deep learning代考|Convolutional Neural Network

CNN [6] 是一种深度学习算法，专门训练用于处理图像文件。一个简单的类，完美地表示 CNN 中的图像，通过一系列卷积层、一个池化层和全连接层进行处理。CNN 可以通过应用来学习图像的多层特征表示

## 机器学习代写|深度学习project代写deep learning代考|Recurrent Neural Network

RNN 是一种既有效又健壮的深度学习技术，它是目前使用的最有前途的方法之一，因为它是唯一具有内部存储的方法。当需要预测序列的下一个单词时，RNN 很有用 [7]。在处理顺序数据（财务数据或 DNA 序列）时，通常使用循环神经网络。原因是模型使用了层，这些层为模型提供了短期记忆。使用此内存，它可以更准确地确定下一个数据并记住有关计算内容的所有信息。如果我们想在此类数据中使用序列匹配，我们需要一个具有数据先前知识的网络。在这种方法中，上一步的输出被馈送到当前步骤。RNN的架构包括三层：输入层，隐藏层和输出层。隐藏层记住有关序列的信息。

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。