图像中的文字在当下相机高速发展下显得尤为重要,人们开始通过拍摄照片直接进行图像上文字的识别,最常用的就是寄快递收寄地址的识别。其中用到的技术是OCR(optical character recognition)字符识别技术,其中文名字叫做光学字符识别。...图像中的文字在当下相机高速发展下显得尤为重要,人们开始通过拍摄照片直接进行图像上文字的识别,最常用的就是寄快递收寄地址的识别。其中用到的技术是OCR(optical character recognition)字符识别技术,其中文名字叫做光学字符识别。它是利用光学技术和计算机技术通过检测字符每个像素的暗、亮模式确定其形状,然后用字符识别方法将形状翻译成计算机文字的过程。随着日常生活网络化的推进,各种纸质文档的数字化智能化识别进程也在加速。经过二十世纪九十年代的发展,对字符识别技术的研究已经取得了很大的进展,市场上目前正在使用的各种OCR识别软件层出不穷。但是以往对证件的识别是一个比较大的难题。文中的研究主要是对普通的文字进行识别。识别系统包括三个模块:图像预处理、图像分割、字符识别。前两个模块又包含图像的二值化分析、灰度化等,对其进行了描述。展开更多
OCR(Optical Character Recognition)是通过检测字符每个像素亮度的模式确定其形状,然后用字符识别方法将形状翻译成计算机文字的过程。文章利用Java语言实现OCR步骤,包括像素二值化,图像分割,训练识别和输出等。测试开发是在web验证码...OCR(Optical Character Recognition)是通过检测字符每个像素亮度的模式确定其形状,然后用字符识别方法将形状翻译成计算机文字的过程。文章利用Java语言实现OCR步骤,包括像素二值化,图像分割,训练识别和输出等。测试开发是在web验证码识别场景中进行的,web验证码是将一串随机产生的符号,生成为图片,再加上一些干扰线,使之能有效防止恶意注册和灌水。通过测试表明,该方法可行、有效;拒识率、误识率低;识别速度快,具有一定的实用意义。展开更多
Health care is an important part of human life and is a right for everyone. One of the most basic human rights is to receive health care whenever they need it. However, this is simply not an option for everyone due to...Health care is an important part of human life and is a right for everyone. One of the most basic human rights is to receive health care whenever they need it. However, this is simply not an option for everyone due to the social conditions in which some communities live and not everyone has access to it. This paper aims to serve as a reference point and guide for users who are interested in monitoring their health, particularly their blood analysis to be aware of their health condition in an easy way. This study introduces an algorithmic approach for extracting and analyzing Complete Blood Count (CBC) parameters from scanned images. The algorithm employs Optical Character Recognition (OCR) technology to process images containing tabular data, specifically targeting CBC parameter tables. Upon image processing, the algorithm extracts data and identifies CBC parameters and their corresponding values. It evaluates the status (High, Low, or Normal) of each parameter and subsequently presents evaluations, and any potential diagnoses. The primary objective is to automate the extraction and evaluation of CBC parameters, aiding healthcare professionals in swiftly assessing blood analysis results. The algorithmic framework aims to streamline the interpretation of CBC tests, potentially improving efficiency and accuracy in clinical diagnostics.展开更多
目的:设计一种基于光学字符识别(optical character recognition,OCR)模型的医疗救治装备数据采集平台,以实现应急灾害救援条件下医疗数据的自动化采集。方法:该平台以医疗物联网“感知—网络—平台”架构为基础构建。首先,选取Raspberr...目的:设计一种基于光学字符识别(optical character recognition,OCR)模型的医疗救治装备数据采集平台,以实现应急灾害救援条件下医疗数据的自动化采集。方法:该平台以医疗物联网“感知—网络—平台”架构为基础构建。首先,选取Raspberry Pi 4B作为边缘节点,使用视频采集卡、摄像头、平板计算机等搭建硬件环境。其次,基于卷积循环神经网络(convolutional recurrent neural network,CRNN)优化OCR模型,通过软硬件协同方式实现医疗终端视频流处理与数据提取。最后,采用FineBI工具实现交互界面设计与数据库链接。结果:经实验验证,该平台的硬件环境可靠、稳定,优化后的OCR模型文本识别准确率提升,且采用该平台能够实现对医疗设备数据的快速、自动化采集。结论:采用该平台能够为医护人员提供全面、准确的医疗救治装备数据支撑,有利于提升医疗救治效率。展开更多
文摘图像中的文字在当下相机高速发展下显得尤为重要,人们开始通过拍摄照片直接进行图像上文字的识别,最常用的就是寄快递收寄地址的识别。其中用到的技术是OCR(optical character recognition)字符识别技术,其中文名字叫做光学字符识别。它是利用光学技术和计算机技术通过检测字符每个像素的暗、亮模式确定其形状,然后用字符识别方法将形状翻译成计算机文字的过程。随着日常生活网络化的推进,各种纸质文档的数字化智能化识别进程也在加速。经过二十世纪九十年代的发展,对字符识别技术的研究已经取得了很大的进展,市场上目前正在使用的各种OCR识别软件层出不穷。但是以往对证件的识别是一个比较大的难题。文中的研究主要是对普通的文字进行识别。识别系统包括三个模块:图像预处理、图像分割、字符识别。前两个模块又包含图像的二值化分析、灰度化等,对其进行了描述。
文摘OCR(Optical Character Recognition)是通过检测字符每个像素亮度的模式确定其形状,然后用字符识别方法将形状翻译成计算机文字的过程。文章利用Java语言实现OCR步骤,包括像素二值化,图像分割,训练识别和输出等。测试开发是在web验证码识别场景中进行的,web验证码是将一串随机产生的符号,生成为图片,再加上一些干扰线,使之能有效防止恶意注册和灌水。通过测试表明,该方法可行、有效;拒识率、误识率低;识别速度快,具有一定的实用意义。
文摘Health care is an important part of human life and is a right for everyone. One of the most basic human rights is to receive health care whenever they need it. However, this is simply not an option for everyone due to the social conditions in which some communities live and not everyone has access to it. This paper aims to serve as a reference point and guide for users who are interested in monitoring their health, particularly their blood analysis to be aware of their health condition in an easy way. This study introduces an algorithmic approach for extracting and analyzing Complete Blood Count (CBC) parameters from scanned images. The algorithm employs Optical Character Recognition (OCR) technology to process images containing tabular data, specifically targeting CBC parameter tables. Upon image processing, the algorithm extracts data and identifies CBC parameters and their corresponding values. It evaluates the status (High, Low, or Normal) of each parameter and subsequently presents evaluations, and any potential diagnoses. The primary objective is to automate the extraction and evaluation of CBC parameters, aiding healthcare professionals in swiftly assessing blood analysis results. The algorithmic framework aims to streamline the interpretation of CBC tests, potentially improving efficiency and accuracy in clinical diagnostics.
文摘目的:设计一种基于光学字符识别(optical character recognition,OCR)模型的医疗救治装备数据采集平台,以实现应急灾害救援条件下医疗数据的自动化采集。方法:该平台以医疗物联网“感知—网络—平台”架构为基础构建。首先,选取Raspberry Pi 4B作为边缘节点,使用视频采集卡、摄像头、平板计算机等搭建硬件环境。其次,基于卷积循环神经网络(convolutional recurrent neural network,CRNN)优化OCR模型,通过软硬件协同方式实现医疗终端视频流处理与数据提取。最后,采用FineBI工具实现交互界面设计与数据库链接。结果:经实验验证,该平台的硬件环境可靠、稳定,优化后的OCR模型文本识别准确率提升,且采用该平台能够实现对医疗设备数据的快速、自动化采集。结论:采用该平台能够为医护人员提供全面、准确的医疗救治装备数据支撑,有利于提升医疗救治效率。
