An eazy code from eazy ciphers

易密码的易密码

Hii!! welcome you all to our first medium story…. Well when I work on image datasets, I always think why these characters displayed in the image can be retrieved in a simple text format.

嗨！ 欢迎大家来到我们的第一个中等故事…。 好吧，当我处理图像数据集时，我总是想为什么可以以简单的文本格式检索图像中显示的这些字符。

So, with that inspiration in mind to retrieve the data present in images. I surfed on the internet, I found a lot of ways to find and extract or recognize the characters present in an image.

因此，考虑到这一灵感，可以检索图像中存在的数据。 我在互联网上冲浪时，发现了很多查找和提取或识别图像中字符的方法。

In that surfing for character recognition, I found some online tools which will take input from the user and give the output.

在那次用于字符识别的冲浪中，我找到了一些在线工具，这些工具将吸收用户的输入并提供输出。

The same way, we thought to simply in much better eazy way that any beginner can understand it.

同样，我们认为以一种更加轻松自如的方式，任何初学者都可以理解它。

字符识别： (Character Recognition:)

Character Recognition process helps in the recognition of each text element with the accuracy of the characters.

字符识别过程有助于以字符的准确性识别每个文本元素。

The accuracy of classification and identification of characters or text elements in an image is measured by applying deep learning algorithms. These characters make a lot of difference in literature.

通过应用深度学习算法，可以测量图像中字符或文本元素的分类和识别准确性。 这些字符在文学上有很大的不同。

When we apply normal random techniques for recognition of characters may give some errors at a particular point. If we use deep learning OpenCV algorithm will give an effective output.

当我们应用正常的随机技术来识别字符时，可能会在特定点出现一些错误。 如果我们使用深度学习，OpenCV算法将给出有效的输出。

For the first way of approach to run our model, one has to install tesseract which is developed by Google as an optical character recognition engine.

对于运行模型的第一种方法，必须安装由Google作为光学字符识别引擎开发的tesseract。

pip install pytesseract

OpenCV： (OpenCV:)

Open CV is a deep learning technique where we mainly used to execute the data by calculating many eliminations of noises.

开放式简历是一种深度学习技术，我们主要通过计算许多消除的噪声来执行数据。

Open CV is the most popularly used algorithm in the branch of deep learning techniques.

Open CV是深度学习技术领域中使用最广泛的算法。

It makes the colossally reliant on the data appeared from the trained characters and helps in recognizing the text present in the image. Open CV makes the missing accuracy of an algorithm into a picture. Gray scaling gives a valid classification in the field of character recognition. So hereby we import all the necessary packages for making our model into a good track.

它极大地依赖于受过训练的角色显示的数据，并有助于识别图像中存在的文本。 开放式简历使算法丢失的准确性变成一幅图画。 灰度等级在字符识别领域提供了有效的分类。 因此，我们在此导入所有必要的软件包，以使我们的模型步入正轨。

In order to install OpenCV in your local computer use this command...

为了在您的本地计算机上安装OpenCV，请使用以下命令...

pip install opencv-python

图像可以直接读取到代码中吗？ (Can an image be directly read to the code?)

Let's discuss what is affecting to recognize the characters:

让我们讨论影响识别字符的因素：

• Noising in an image causes many factors of false recognition of characters. for ensuring noise-free in our recognition eliminate in code.图像中的噪声会导致许多错误识别字符的因素。 为确保无噪音，我们在代码中消除了识别。
• When the image is not high resolution then identification fails. So, it is better to take a high-resolution image for accuracy in the result.当图像不是高分辨率时，识别将失败。 因此，为获得准确的结果，最好拍摄高分辨率的图像。
• Some times the angle of the image also defects.有时图像的角度也会出现缺陷。
• Reflection of an image makes incorrect in the assumption of text. It makes to identify the letter “F” as “P” like that so many.图像的反射使假定的文本不正确。 如此多的字母“ F”被识别为“ P”。
• Fonts also vary the results if the code isn’t able to train it.如果代码无法训练字体，字体也会改变结果。
• Various images have various styles of representation of the art, so, when there is more color complexion or multiple colors make incorrect assumptions of recognition text in an image.各种图像具有本领域的各种样式，因此，当出现更多肤色或多种颜色时，就会对图像中的识别文本做出不正确的假设。

Considering all the above factors in mind one have to build the code accordingly where all the test cases passes.

考虑到以上所有因素，必须在所有测试用例通过的地方相应地构建代码。

使用OpenCV识别字符的Python代码： (Python code for recognizing characters using OpenCV:)

This code can be downloaded for your easy understanding of approach to the recognition.

可以下载此代码，以方便您理解识别方法。

Importing all the packages:

导入所有软件包：

#import all the packagesimport cv2import numpy as npimport pytesseractfrom PIL import Image

once you install with the packages import those into code.

使用软件包安装后，将其导入代码。

Declare or initialize the path:

声明或初始化路径：

A tesseract is an open-source tool it can be downloaded from online. After downloading mention its path like below.

tesseract是一种开源工具，可以从网上下载。 下载后，请提及其路径，如下所示。

# path of pytesseract execution folder pytesseract.pytesseract.tesseract_cmd = 'C:\\Program Files\\Tesseract-OCR\\tesseract.exe'# Path of imagemain_path = r'qu12.png'

Main Method:

主要方法：

In this part of code, we are implementing

在这部分代码中，我们正在实现

• Read the image读取图像
• Greyscale the image灰度图像
• Do dilation and erosion to eliminate unwanted noises in the picture.进行膨胀和腐蚀以消除图像中不需要的噪声。
• Write the image after the removal of the noise.去除噪点后写图像。
• Apply the threshold to get the only black and white pictures.应用阈值以获取唯一的黑白图片。
• Write the same image for the further recognition process.写入相同的图像以进行进一步的识别过程。
• Character recognition with Tesseract.使用Tesseract进行字符识别。

def get_string(pic_path):    # Reading picture with opencv    pic = cv2.imread(pic_path)# grey-scale the picture    pic = cv2.cvtColor(pic, cv2.COLOR_BGR2GRAY)# Do dilation and erosion to eliminate unwanted noises    kernel = np.ones((1, 1), np.uint8)    pic = cv2.dilate(pic, kernel, iterations=20)    pic = cv2.erode(pic, kernel, iterations=20)# Write image after removed noise    cv2.imwrite(main_path + "no_noise.png", pic)#  threshold applying to get only black and white picture     pic = cv2.adaptiveThreshold(pic, 300, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2)# Write the image for later recognition process     cv2.imwrite(main_path + "threshold.png", pic)# Character recognition with tesseract    final = pytesseract.image_to_string(Image.open(main_path + "threshold.png"))return final

Display the final output:

显示最终输出：

print(get_string(src_path))

Inputs and outputs:

输入和输出：

Here we give the inputs which we have given to our code and outputs which we got later execution of our code.

在这里，我们给出输入到代码中的输入和输出，以便稍后执行代码。

Input:

输入：

Output:

输出：

Our GitHub account for your reference in the code handling: https://github.com/eazyciphers/deep-learning-tutors

我们的GitHub帐户供您在代码处理中参考： https : //github.com/eazyciphers/deep-learning-tutors

For more updates do follow Eazy Ciphers

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