OCR
•Download as PPT, PDF•
2 likes•6,038 views
Artificial neural networks are commonly used in optical character recognition algorithms due to their flexibility, ability to learn, and power. ANNs work by taking an input, running it through a network of neurons arranged in layers, and producing an output. They can be trained to recognize patterns through a learning stage where they are given many examples of input and output pairs. Once trained, ANNs can accurately evaluate new inputs and recognize characters at a 98% rate with only 5% error. Common types of ANNs include feedforward, recurrent, radial basis function, and self-organizing networks.
Report
Share
Report
Share
![OCR Algorithms ,[object Object]](https://image.slidesharecdn.com/ocr-100309030407-phpapp02/85/OCR-1-320.jpg)
![OCR ,[object Object],[object Object],KAZ 813](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
OCR 's Functions
This document discusses optical character recognition (OCR) technology. OCR software scans printed text documents and converts them into editable, machine-readable text files. The document outlines the benefits of OCR such as eliminating manual data entry and improving data accuracy. It also lists key areas where OCR is used including cloud storage, mailroom automation, and banking. Metrics for evaluating OCR software and current trends in OCR technology are discussed. Top OCR providers like Adobe Acrobat, OmniPage, and ABBYY FineReader are compared. Case studies on how OCR is used by accountants and universities are provided.
Optical Character Recognition (OCR)
Optical Character Recognition (OCR) involves the conversion of scanned images of printed text into machine-readable text. It is heavily used in industry for applications like editing, scanning, searching, and compact storage. The document discusses developing an OCR system using machine learning, artificial intelligence, and neural networks to recognize characters despite variations in image quality, orientation, and language. It outlines the technologies, current progress implementing linear and logistic regression models, and plans for character segmentation and feature extraction.
Optical character recognition (ocr) ppt
The document discusses optical character recognition (OCR), which is the process of converting scanned images of printed or handwritten text into machine-encoded text. It provides a brief history of OCR, explaining some of the early developments. It also outlines the typical steps involved, including pre-processing, character recognition, and post-processing. Examples of applications of OCR technology are given.
A STUDY ON OPTICAL CHARACTER RECOGNITION TECHNIQUES
Optical Character Recognition (OCR) is the process which enables a system to without human intervention
identifies the scripts or alphabets written into the users’ verbal communication. Optical Character
identification has grown to be individual of the mainly flourishing applications of knowledge in the field of
pattern detection and artificial intelligence. In our survey we study on the various OCR techniques. In this
paper we resolve and examine the hypothetical and numerical models of Optical Character Identification.
The Optical character identification or classification (OCR) and Magnetic Character Recognition (MCR)
techniques are generally utilized for the recognition of patterns or alphabets. In general the alphabets are
in the variety of pixel pictures and it could be either handwritten or stamped, of any series, shape or
direction etc. Alternatively in MCR the alphabets are stamped with magnetic ink and the studying machine
categorize the alphabet on the basis of the exclusive magnetic field that is shaped by every alphabet. Both
MCR and OCR discover utilization in banking and different trade appliances. Earlier exploration going on
Optical Character detection or recognition has shown that the In Handwritten text there is no limitation
lying on the script technique. Hand written correspondence is complicated to be familiar through due to
diverse human handwriting style, disparity in angle, size and shape of calligraphy. An assortment of
approaches of Optical Character Identification is discussed here all along through their achievement.
Optical Character Recognition Using Python
Optical Character Recognition is a process of classifying optical patterns with respect to alphanumeric or other characters. It also includes segmentation, feature extraction and classification. Deep learning is part of a broader family of machine learning methods based on artificial neural networks with. representation learning The idea of the project is to extract text from image using Deep Learning by OCR Ponvizhi. U | Ramya. P | Ramya. R "Optical Character Recognition Using Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd41099.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/41099/optical-character-recognition-using-python/ponvizhi-u
OCR speech using Labview
This document discusses an OCR-based speech synthesis system developed using LabVIEW 2013. The system has two main parts: optical character recognition and text-to-speech conversion. It uses a digital camera to capture images, performs preprocessing like binarization, then matches characters to a template for recognition. The recognized text is converted to speech using text-to-speech synthesis for audio output. The system achieves 75-80% accuracy but could be improved with support for more fonts and font sizes.
Optical Character Recognition
Optical Character Recognition (OCR) is a technology that converts scanned documents and images into editable text. OCR works by identifying character shapes through image processing and noise removal techniques, then converts the recognized characters into binary encodings like Unicode. The history of OCR began in 1914 with Emanuel Goldberg's machine for translating characters into telegraph code. Major advances included Ray Kurzweil's reading machine for the blind in the 1970s and the commercialization of OCR software in the late 20th century. OCR has many advantages, allowing text from paper documents and images to be easily searched, edited and reused digitally. Recommended OCR software includes Google Drive, Nuance OmniPage, and Abbyy
Optical Character Recognition (OCR) System
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Recommended
OCR 's Functions
This document discusses optical character recognition (OCR) technology. OCR software scans printed text documents and converts them into editable, machine-readable text files. The document outlines the benefits of OCR such as eliminating manual data entry and improving data accuracy. It also lists key areas where OCR is used including cloud storage, mailroom automation, and banking. Metrics for evaluating OCR software and current trends in OCR technology are discussed. Top OCR providers like Adobe Acrobat, OmniPage, and ABBYY FineReader are compared. Case studies on how OCR is used by accountants and universities are provided.
Optical Character Recognition (OCR)
Optical Character Recognition (OCR) involves the conversion of scanned images of printed text into machine-readable text. It is heavily used in industry for applications like editing, scanning, searching, and compact storage. The document discusses developing an OCR system using machine learning, artificial intelligence, and neural networks to recognize characters despite variations in image quality, orientation, and language. It outlines the technologies, current progress implementing linear and logistic regression models, and plans for character segmentation and feature extraction.
Optical character recognition (ocr) ppt
The document discusses optical character recognition (OCR), which is the process of converting scanned images of printed or handwritten text into machine-encoded text. It provides a brief history of OCR, explaining some of the early developments. It also outlines the typical steps involved, including pre-processing, character recognition, and post-processing. Examples of applications of OCR technology are given.
A STUDY ON OPTICAL CHARACTER RECOGNITION TECHNIQUES
Optical Character Recognition (OCR) is the process which enables a system to without human intervention
identifies the scripts or alphabets written into the users’ verbal communication. Optical Character
identification has grown to be individual of the mainly flourishing applications of knowledge in the field of
pattern detection and artificial intelligence. In our survey we study on the various OCR techniques. In this
paper we resolve and examine the hypothetical and numerical models of Optical Character Identification.
The Optical character identification or classification (OCR) and Magnetic Character Recognition (MCR)
techniques are generally utilized for the recognition of patterns or alphabets. In general the alphabets are
in the variety of pixel pictures and it could be either handwritten or stamped, of any series, shape or
direction etc. Alternatively in MCR the alphabets are stamped with magnetic ink and the studying machine
categorize the alphabet on the basis of the exclusive magnetic field that is shaped by every alphabet. Both
MCR and OCR discover utilization in banking and different trade appliances. Earlier exploration going on
Optical Character detection or recognition has shown that the In Handwritten text there is no limitation
lying on the script technique. Hand written correspondence is complicated to be familiar through due to
diverse human handwriting style, disparity in angle, size and shape of calligraphy. An assortment of
approaches of Optical Character Identification is discussed here all along through their achievement.
Optical Character Recognition Using Python
Optical Character Recognition is a process of classifying optical patterns with respect to alphanumeric or other characters. It also includes segmentation, feature extraction and classification. Deep learning is part of a broader family of machine learning methods based on artificial neural networks with. representation learning The idea of the project is to extract text from image using Deep Learning by OCR Ponvizhi. U | Ramya. P | Ramya. R "Optical Character Recognition Using Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd41099.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/41099/optical-character-recognition-using-python/ponvizhi-u
OCR speech using Labview
This document discusses an OCR-based speech synthesis system developed using LabVIEW 2013. The system has two main parts: optical character recognition and text-to-speech conversion. It uses a digital camera to capture images, performs preprocessing like binarization, then matches characters to a template for recognition. The recognized text is converted to speech using text-to-speech synthesis for audio output. The system achieves 75-80% accuracy but could be improved with support for more fonts and font sizes.
Optical Character Recognition
Optical Character Recognition (OCR) is a technology that converts scanned documents and images into editable text. OCR works by identifying character shapes through image processing and noise removal techniques, then converts the recognized characters into binary encodings like Unicode. The history of OCR began in 1914 with Emanuel Goldberg's machine for translating characters into telegraph code. Major advances included Ray Kurzweil's reading machine for the blind in the 1970s and the commercialization of OCR software in the late 20th century. OCR has many advantages, allowing text from paper documents and images to be easily searched, edited and reused digitally. Recommended OCR software includes Google Drive, Nuance OmniPage, and Abbyy
Optical Character Recognition (OCR) System
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Optical Character Recognition( OCR )
Optical character recognition (OCR) is the conversion of images of typed or printed text into machine-encoded text. The document discusses OCR including defining it, describing its problem overview, types, steps in the OCR process like pre-processing and character recognition, accuracy considerations, use of free OCR software, pros and cons, and areas for further research like improving recognition of cursive text.
Project report of OCR Recognition
To develop character recognition software system to perform Document Image Analysis which transforms documents in paper format to electronic format
OCR Text Extraction
This research tries to find out amethodology through which any data from the daily-use printed bills and invoices can be extracted. The data from these bills or invoices can be used extensively later on –such as machine learning or statistical analysis. This research focuses on extraction of final bill-amount, itinerary, date and similar data from bills and invoices as they encapsulate an ample amount of information about the users purchases, likes or dislikes etc. Optical Character Recognition (OCR) technology is a system that provides a full alphanumeric recognition of printed or handwritten characters from images. Initially, OpenCV has been used to detect the bill or invoice from the image and filter out the unnecessary noise from the image. Then intermediate image is passed for further processing using Tesseract OCR engine, which is an optical character recognition engine. Tesseract intends to apply Text Segmentation in order to extract written text in various fonts and languages. Our methodology proves to be highly accurate while tested on a variety of input images of bills and invoices.
OCR (Optical Character Recognition)
OCR (Optical Character Recognition) is a technology that recognizes text within digital images. It examines text in documents and converts characters into machine-readable code. OCR is commonly used to convert printed paper documents into editable digital text files. The basic process involves preprocessing the image to clean it up, isolating individual characters, and using character recognition libraries or more advanced techniques to identify each character and assign it the corresponding text. OCR is needed to convert scanned documents into text-searchable files that can be edited, searched, and managed more easily within document systems.
Optical character recognition IEEE Paper Study
Optical character recognition (OCR) is a technology that converts images of typed, handwritten or printed text into machine-encoded text. The document describes the OCR process which includes image pre-processing, segmentation, feature extraction and recognition using a multi-layer perceptron neural network. It discusses advantages such as increased efficiency and ability to instantly search text. Disadvantages include issues with low quality documents. Applications include data entry for business documents and making printed documents searchable.
Basics of-optical-character-recognition
I have presented the power point presentation on Basics of the optical character recognition. Here i have focused to discuss about hoe OCR is used in scanning process and can it be used for document scanning and its uses.
Text reader [OCR]
Optical character recognition technology.
Mini project review.
Documentation and ppt of the ocr(optical character recognition)
Optical Character Recognition (OCR) based Retrieval
The document outlines research works on optical character recognition (OCR) systems, including both global and local (Amharic language) research. It discusses several local studies from 1997-2011 focused on developing OCR for printed, typewritten and handwritten Amharic text. The studies explored various preprocessing, segmentation, recognition algorithms and achieved recognition accuracy rates ranging from 15-99% depending on the type of Amharic text and techniques used. Future research directions included improving techniques for formatted text, different font styles and improving accuracy.
Ocr abstract
The document describes a project to develop optical character recognition (OCR) software for recognizing online and offline handwritten text in multiple languages. It aims to recognize characters from scanned documents or real-time handwriting input and create a user profile. The system scope includes recognizing handwriting from multiple users and cursive script. It will store recognized characters in a text file and optionally convert words to audio for reading documents aloud. The document provides details on OCR technology, applications, literature review, user and system requirements, and the project's goal of using OCR for applications like forms processing.
Optical Character Recognition
Presentation on the New Technology based on the recognition of letters that would be available on Soft and Hard copy both and allow all the format in Soft Copy. Optical character Recognition based on the recognition of letters with all the existing languages.
Final Report on Optical Character Recognition
1. The document discusses optical character recognition (OCR), including its applications, how it works, and the platform used.
2. OCR involves using software to convert scanned images of text into machine-encoded text by recognizing glyphs and classifying characters through feature extraction and neural networks.
3. The authors explore using OCR for tasks like digitization and security monitoring to reduce human error, and discuss future enhancements like recognizing multiple characters and improving accuracy.
Optical Character Recognition: the What, Why, and How
Delivered by Mackenzie Brooks and Alston Cobourn to Washington and Lee University. This presentation explains what OCR is, gives a variety of use cases, and covers the types of tools available.
ocr
The document presents a presentation on character recognition and conversion. It discusses the purpose of character recognition as document processing and speeding up recognition. It describes the architecture as containing templates, scanning, recognition, and coding. It details testing through sample and performance testing, showing the conversion of various images to text. It concludes by discussing applications and limitations of character recognition technology.
Machine learning
The document discusses machine learning and optical character recognition (OCR). It defines machine learning as the study of algorithms that can learn from data without being explicitly programmed. It discusses the types of machine learning algorithms and applications such as spam detection and medical research. The aim of the presentation is to implement OCR using supervised machine learning to convert images of text into machine-encoded text. It describes the OCR process and prospects for using OCR in applications like processing business documents.
Design and implementation of optical character recognition using template mat...
Abstract
Optical character recognition (OCR) is an efficient way of converting scanned image into machine code which can further edit. There are variety of methods have been implemented in the field of character recognition. This paper proposes Optical character recognition by using Template Matching. The templates formed, having variety of fonts and size .In this proposed system, Image pre-processing, Feature extraction and classification algorithms have been implemented so as to build an excellent character recognition technique for different scripts .Result of this approach is also discussed in this paper. This system is implemented in Matlab.
Keywords- OCR, Feature Extraction, Classification
OCR2
The document discusses Optical Character Recognition (OCR) and describes the key steps and algorithms involved. It summarizes the main modules in an OCR system including pre-processing, feature extraction, classification, and post-processing. It then discusses two specific algorithms - Principal Component Analysis and Learning Vector Quantization - that can be used to implement OCR. The document also evaluates the feasibility and provides a high-level design for an OCR system including graphical user interface, scanner, training, and main modules.
OCR, OMR and Track Ball Working Principals
Presented by Rida Khan,Safa Aamir & Shehrbano Lakhanie, this is a very precise the powerful presentation on working principals of OCR, OMR and Track Ball.
05a
This document provides an overview of optical character recognition (OCR) including system requirements, advantages and disadvantages, operation and management, questionnaire design, OCR field operation, and country outlook. It describes OCR as a system that scans printed or handwritten text and converts it into machine-readable text. Key aspects covered include scanner specifications, recognition software, training needs, error sources and mitigation, and common practices in countries using OCR for censuses.
Ocr algorithm for ge’ez characters
This document proposes developing an optical character recognition (OCR) algorithm for Ge'ez characters. It begins with an introduction to the Ge'ez script and its use in Ethiopia and Eritrea. It then discusses what OCR is, when and why it is used, and the problem of digitizing Ethiopia's historical documents written in Ge'ez. The authors propose using OCR technology and artificial neural networks to develop an application that can recognize Ge'ez characters from images. Such an algorithm could be used to digitize important religious and cultural documents currently only available on paper, helping preserve Ethiopia's intellectual property. Results and conclusion sections indicate the algorithm would make digitization of Ge'ez texts more feasible while a
A brief introduction to OCR (Optical character recognition)
These slides include the answers for the following questions:
- What is OCR?
- Why do we need it?
- Why is it difficult?
- Comparison between OCR & object detections
- Three approaches for text localization
- Three approaches for text recognition
Videos are also available from the below:
(Korean) https://youtu.be/ckRFBl_XWFg
(English) coming soon
[Reference] Hwalsuk Lee, https://www.slideshare.net/deview/111-ai
Will Machines Replace Man? AI
This document discusses the usage of artificial intelligence. It provides an overview of artificial intelligence including definitions of AI and intelligence, technologies used in AI such as neural networks and genetic algorithms, and applications of AI in fields like computer science, finance, hospitals, and transportation. The document also notes some limitations of current AI approaches including being data-specific and not scaling well to real-world complex domains. It aims to provide a broad introduction to the concepts and applications of artificial intelligence.
Fossasia ai-ml technologies and application for product development-chetan kh...
Train at GPU and Inference at Mobile, Artificial Intelligence / Machine learning Technologies and Applications for AI Driven Product Development. Talk at FOSSASIA 2018, Singapore
More Related Content
What's hot
Optical Character Recognition( OCR )
Optical character recognition (OCR) is the conversion of images of typed or printed text into machine-encoded text. The document discusses OCR including defining it, describing its problem overview, types, steps in the OCR process like pre-processing and character recognition, accuracy considerations, use of free OCR software, pros and cons, and areas for further research like improving recognition of cursive text.
Project report of OCR Recognition
To develop character recognition software system to perform Document Image Analysis which transforms documents in paper format to electronic format
OCR Text Extraction
This research tries to find out amethodology through which any data from the daily-use printed bills and invoices can be extracted. The data from these bills or invoices can be used extensively later on –such as machine learning or statistical analysis. This research focuses on extraction of final bill-amount, itinerary, date and similar data from bills and invoices as they encapsulate an ample amount of information about the users purchases, likes or dislikes etc. Optical Character Recognition (OCR) technology is a system that provides a full alphanumeric recognition of printed or handwritten characters from images. Initially, OpenCV has been used to detect the bill or invoice from the image and filter out the unnecessary noise from the image. Then intermediate image is passed for further processing using Tesseract OCR engine, which is an optical character recognition engine. Tesseract intends to apply Text Segmentation in order to extract written text in various fonts and languages. Our methodology proves to be highly accurate while tested on a variety of input images of bills and invoices.
OCR (Optical Character Recognition)
OCR (Optical Character Recognition) is a technology that recognizes text within digital images. It examines text in documents and converts characters into machine-readable code. OCR is commonly used to convert printed paper documents into editable digital text files. The basic process involves preprocessing the image to clean it up, isolating individual characters, and using character recognition libraries or more advanced techniques to identify each character and assign it the corresponding text. OCR is needed to convert scanned documents into text-searchable files that can be edited, searched, and managed more easily within document systems.
Optical character recognition IEEE Paper Study
Optical character recognition (OCR) is a technology that converts images of typed, handwritten or printed text into machine-encoded text. The document describes the OCR process which includes image pre-processing, segmentation, feature extraction and recognition using a multi-layer perceptron neural network. It discusses advantages such as increased efficiency and ability to instantly search text. Disadvantages include issues with low quality documents. Applications include data entry for business documents and making printed documents searchable.
Basics of-optical-character-recognition
I have presented the power point presentation on Basics of the optical character recognition. Here i have focused to discuss about hoe OCR is used in scanning process and can it be used for document scanning and its uses.
Text reader [OCR]
Optical character recognition technology.
Mini project review.
Documentation and ppt of the ocr(optical character recognition)
Optical Character Recognition (OCR) based Retrieval
The document outlines research works on optical character recognition (OCR) systems, including both global and local (Amharic language) research. It discusses several local studies from 1997-2011 focused on developing OCR for printed, typewritten and handwritten Amharic text. The studies explored various preprocessing, segmentation, recognition algorithms and achieved recognition accuracy rates ranging from 15-99% depending on the type of Amharic text and techniques used. Future research directions included improving techniques for formatted text, different font styles and improving accuracy.
Ocr abstract
The document describes a project to develop optical character recognition (OCR) software for recognizing online and offline handwritten text in multiple languages. It aims to recognize characters from scanned documents or real-time handwriting input and create a user profile. The system scope includes recognizing handwriting from multiple users and cursive script. It will store recognized characters in a text file and optionally convert words to audio for reading documents aloud. The document provides details on OCR technology, applications, literature review, user and system requirements, and the project's goal of using OCR for applications like forms processing.
Optical Character Recognition
Presentation on the New Technology based on the recognition of letters that would be available on Soft and Hard copy both and allow all the format in Soft Copy. Optical character Recognition based on the recognition of letters with all the existing languages.
Final Report on Optical Character Recognition
1. The document discusses optical character recognition (OCR), including its applications, how it works, and the platform used.
2. OCR involves using software to convert scanned images of text into machine-encoded text by recognizing glyphs and classifying characters through feature extraction and neural networks.
3. The authors explore using OCR for tasks like digitization and security monitoring to reduce human error, and discuss future enhancements like recognizing multiple characters and improving accuracy.
Optical Character Recognition: the What, Why, and How
Delivered by Mackenzie Brooks and Alston Cobourn to Washington and Lee University. This presentation explains what OCR is, gives a variety of use cases, and covers the types of tools available.
ocr
The document presents a presentation on character recognition and conversion. It discusses the purpose of character recognition as document processing and speeding up recognition. It describes the architecture as containing templates, scanning, recognition, and coding. It details testing through sample and performance testing, showing the conversion of various images to text. It concludes by discussing applications and limitations of character recognition technology.
Machine learning
The document discusses machine learning and optical character recognition (OCR). It defines machine learning as the study of algorithms that can learn from data without being explicitly programmed. It discusses the types of machine learning algorithms and applications such as spam detection and medical research. The aim of the presentation is to implement OCR using supervised machine learning to convert images of text into machine-encoded text. It describes the OCR process and prospects for using OCR in applications like processing business documents.
Design and implementation of optical character recognition using template mat...
Abstract
Optical character recognition (OCR) is an efficient way of converting scanned image into machine code which can further edit. There are variety of methods have been implemented in the field of character recognition. This paper proposes Optical character recognition by using Template Matching. The templates formed, having variety of fonts and size .In this proposed system, Image pre-processing, Feature extraction and classification algorithms have been implemented so as to build an excellent character recognition technique for different scripts .Result of this approach is also discussed in this paper. This system is implemented in Matlab.
Keywords- OCR, Feature Extraction, Classification
OCR2
The document discusses Optical Character Recognition (OCR) and describes the key steps and algorithms involved. It summarizes the main modules in an OCR system including pre-processing, feature extraction, classification, and post-processing. It then discusses two specific algorithms - Principal Component Analysis and Learning Vector Quantization - that can be used to implement OCR. The document also evaluates the feasibility and provides a high-level design for an OCR system including graphical user interface, scanner, training, and main modules.
OCR, OMR and Track Ball Working Principals
Presented by Rida Khan,Safa Aamir & Shehrbano Lakhanie, this is a very precise the powerful presentation on working principals of OCR, OMR and Track Ball.
05a
This document provides an overview of optical character recognition (OCR) including system requirements, advantages and disadvantages, operation and management, questionnaire design, OCR field operation, and country outlook. It describes OCR as a system that scans printed or handwritten text and converts it into machine-readable text. Key aspects covered include scanner specifications, recognition software, training needs, error sources and mitigation, and common practices in countries using OCR for censuses.
Ocr algorithm for ge’ez characters
This document proposes developing an optical character recognition (OCR) algorithm for Ge'ez characters. It begins with an introduction to the Ge'ez script and its use in Ethiopia and Eritrea. It then discusses what OCR is, when and why it is used, and the problem of digitizing Ethiopia's historical documents written in Ge'ez. The authors propose using OCR technology and artificial neural networks to develop an application that can recognize Ge'ez characters from images. Such an algorithm could be used to digitize important religious and cultural documents currently only available on paper, helping preserve Ethiopia's intellectual property. Results and conclusion sections indicate the algorithm would make digitization of Ge'ez texts more feasible while a
A brief introduction to OCR (Optical character recognition)
These slides include the answers for the following questions:
- What is OCR?
- Why do we need it?
- Why is it difficult?
- Comparison between OCR & object detections
- Three approaches for text localization
- Three approaches for text recognition
Videos are also available from the below:
(Korean) https://youtu.be/ckRFBl_XWFg
(English) coming soon
[Reference] Hwalsuk Lee, https://www.slideshare.net/deview/111-ai
What's hot (20)
Optical Character Recognition (OCR) based Retrieval
Optical Character Recognition (OCR) based Retrieval
Optical Character Recognition: the What, Why, and How
Optical Character Recognition: the What, Why, and How
Design and implementation of optical character recognition using template mat...
Design and implementation of optical character recognition using template mat...
A brief introduction to OCR (Optical character recognition)
A brief introduction to OCR (Optical character recognition)
Similar to OCR
Will Machines Replace Man? AI
This document discusses the usage of artificial intelligence. It provides an overview of artificial intelligence including definitions of AI and intelligence, technologies used in AI such as neural networks and genetic algorithms, and applications of AI in fields like computer science, finance, hospitals, and transportation. The document also notes some limitations of current AI approaches including being data-specific and not scaling well to real-world complex domains. It aims to provide a broad introduction to the concepts and applications of artificial intelligence.
Fossasia ai-ml technologies and application for product development-chetan kh...
Train at GPU and Inference at Mobile, Artificial Intelligence / Machine learning Technologies and Applications for AI Driven Product Development. Talk at FOSSASIA 2018, Singapore
Computer vision, machine, and deep learning
This document provides an overview of computer vision, machine learning, and deep learning with Python. It introduces computer vision and some example applications like optical character recognition and face detection. It then discusses machine learning and how it can be applied to computer vision problems. Deep learning is introduced as a type of machine learning using artificial neural networks. Examples of successful deep learning applications are presented, including speech recognition and the AlphaGo program that mastered the game of Go. Finally, Python is discussed as a programming language well-suited for scientific and deep learning applications due to supporting libraries like NumPy, Scipy, and Matplotlib.
Artificial Intelligence
The document discusses artificial intelligence, including its history, applications, and languages. It provides an overview of AI, noting that it aims to recreate human intelligence through machine learning and problem solving. The document then covers key topics like the philosophy of AI, limits on machine intelligence, and comparisons between human and artificial brains. It also gives brief histories of AI and machine learning. The document concludes by discussing popular AI programming languages like Lisp and Prolog, as well as various applications of AI technologies.
HKOSCon18 - Chetan Khatri - Open Source AI / ML Technologies and Application ...
This document summarizes a presentation about open source AI and machine learning technologies for product development. The presentation discusses key concepts like artificial intelligence, machine learning, deep learning and neural networks. It also provides examples of using computer vision, natural language processing and other AI techniques for applications like self-driving cars, visual search, sentiment analysis and more. Challenges in scaling models and frameworks are discussed along with solutions like ONNX for model interoperability across platforms.
Microsoft AI at SAI '17
AI, breakthroughs and (ethical) challenges and how Microsoft can help your organization doing more with data.
AI & ML
This document provides information about social media links, an introduction to artificial intelligence and machine learning, and modules for an AI and ML course. It includes Karan Shaw's social media links and background. It then defines AI as systems that mimic human behavior through understanding how humans think and learn. Machine learning is described as systems that can learn from experience without being explicitly programmed. Finally, it outlines 15 modules that will be covered in the course, including introductions to AI and ML, different AI techniques, supervised and unsupervised learning, and linear regression models.
Machine learning in startup
This document discusses machine learning and artificial intelligence. It provides definitions of both from various experts, with machine learning described as the algorithms that enable AI by making machines smarter. The document also lists some common machine learning techniques like computer vision and natural language processing. It discusses the promise of automation through exponential technologies and lists several popular machine learning tools and frameworks like TensorFlow, IBM Watson, and Scikit Learn. Quotes are provided on the relationship between tools and humanity.
Filip Maertens - AI, Machine Learning and Chatbots: Think AI-first
Filip Maertens presented this "AI, Machine Learning and Chatbots" at the "Future of IT" seminar on 20th of September 2017 in Brussels. Twitter: @fmaertens Email: filip@faction.xyz
Artificial intelligence
Power-Point Presentation on Artificial Intelligence for All Students Technical or Non-Technical in simple Words for understanding.
Sentiment Analysis Using Solr
Solr is an open source, widely used, popular IR machine. It can be used for simple sentiment analysis and sentiment retrieval tool. Its multi-language analyzers together with UIMA (Unstructured Information Management Architecture) framework can be extended for sentiment extraction. Each sentence passes through a series of pluggable annotators. Entity and its associated polarity are detected for each sentence. Polarity of each sentence is stored into Solr index. Persistent model files can be created from training data and accessed at run time.
Presentation v3
The purpose of this workshop was to highlight the the significance of AI, IoT and their integration under the light of scientific research. The presentation of the workshop can be found below.
Deep learning: the final frontier for time series analysis and signal process...
- Deep learning, specifically convolutional neural networks and recurrent neural networks, provides alternatives to traditional time series and signal processing techniques that can learn patterns without specifying a model.
- CNNs can learn local geometrical patterns in time series while retaining information over time. RNNs can learn long-range dependencies in high-dimensional data.
- Deep learning approaches like autoencoders are well-suited for tasks like pattern matching, anomaly detection, and simulation/generation of time series. Hybrid approaches that combine hand-crafted features with deep learning may also be effective.
An introduction to AI (artificial intelligence)
An introduction to AI (artificial intelligence)
The ppt link is available bellow https://docs.google.com/presentation/d/1-oaO75DEdP259HNrrvh5fbZVOtaiiiffi3luyv0tShw/edit?usp=sharing
you could leave your comments on google slides
Artificial Intelligence by GK
John McCarthy is considered the founder of the field of artificial intelligence. In 1955, he organized the Dartmouth conference which brought together leading researchers and laid the foundation for the field of AI. McCarthy was a professor of computer science at Stanford University. Some of the key programming languages developed for AI include Lisp, Prolog, and STRIPS. The applications of AI include natural language processing, expert systems, planning and robotics, machine learning, and game playing.
Presentation.ai
Artificial intelligence can help with speech recognition by recreating and enhancing the human speech recognition process. Speech recognition systems allow spoken words to be converted to text that can then be used in applications like word processors. The speech recognition process involves converting an input speech signal to a sequence of feature vectors, using pattern classification to decode the vectors into words, and using acoustic and language models to identify the most likely words. Applications of speech recognition include controlling devices with voice instead of hands and military voice commands. Risks of advanced AI include job losses, changes to leisure time and privacy, and potentially the end of humanity.
Artificial Intelligence Research
This document contains an agenda and information about an upcoming TechData MeetUp on artificial intelligence research. The agenda includes an introduction, discussions on the benefits of a Ph.D. in computer science, hot topics in AI, and must-have skills for AI research. It also covers how to publish research results. The document provides background on the speaker, Nigar Alışzadə, and her education and work experience in computer science and AI research. It also answers common questions about getting a Ph.D. in computer science and the job opportunities after. Additional sections define artificial intelligence and various AI fields like machine learning, neural networks, computer vision, natural language processing, the internet of things, and recommendation engines.
Artifitial intelligence (ai) all in one
Artificial intelligence (AI) all in one presentation consists of almost all concepts of Artificial intelligence. i.e.
Artificial intelligence, Robotics, ANN, NLP, NLU, History of AI, History, Pakistan, basharat jehan, agriculture university peshawar, Forward and Backward Chaining, Grammers in AI, Morphology, Examples of Expert system, Laws of Robotics,Expert system languages, Syntactic Analysis in NLP, Lecture Notes
syllabus-IS.doc
This document outlines the course details for an "Intelligent Systems" course, including 16 lectures and 8 practical works covering topics such as knowledge representation methods, expert systems, machine learning, natural language processing, intelligent robots, and the future of artificial intelligence. The course is taught by Professor Dr. Andrey V. Gavrilov and will provide students with basic concepts of different intelligent systems development methods and tools. Grades will be based on a midterm exam worth 50% and a final exam worth 50% of the total grade.
syllabus-IS.doc
This document outlines the course details for an "Intelligent Systems" course including 16 lectures and 8 practical works covering topics such as knowledge representation methods, expert systems, machine learning, natural language processing, intelligent robots, and the future of artificial intelligence. The course is taught by Professor Dr. Andrey V. Gavrilov and will provide students with basic concepts of different intelligent systems development methods and tools. Grades will be based on a midterm exam worth 50% and a final exam worth 50% of the total grade.
Similar to OCR (20)
Fossasia ai-ml technologies and application for product development-chetan kh...
Fossasia ai-ml technologies and application for product development-chetan kh...
HKOSCon18 - Chetan Khatri - Open Source AI / ML Technologies and Application ...
HKOSCon18 - Chetan Khatri - Open Source AI / ML Technologies and Application ...
Filip Maertens - AI, Machine Learning and Chatbots: Think AI-first
Filip Maertens - AI, Machine Learning and Chatbots: Think AI-first
Deep learning: the final frontier for time series analysis and signal process...
Deep learning: the final frontier for time series analysis and signal process...
Recently uploaded
Benner "Expanding Pathways to Publishing Careers"
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
Leveraging Generative AI to Drive Nonprofit Innovation
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
How to Make a Field Mandatory in Odoo 17
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...Nguyen Thanh Tu Collection
https://app.box.com/s/tacvl9ekroe9hqupdnjruiypvm9rdaneLAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Wound healing PPT
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...Nguyen Thanh Tu Collection
https://app.box.com/s/qhtvq32h4ybf9t49ku85x0n3xl4jhr15What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Aberdeen
How to deliver Powerpoint Presentations.pptx
"How to make and deliver dynamic presentations by making it more interactive to captivate your audience attention"
Gender and Mental Health - Counselling and Family Therapy Applications and In...
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
Physical pharmaceutics notes for B.pharm students
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
Practical manual for National Examination Council, Nigeria.
Contains guides on answering questions on the specimens provided
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
Recently uploaded (20)
Leveraging Generative AI to Drive Nonprofit Innovation
Leveraging Generative AI to Drive Nonprofit Innovation
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
BÀI TẬP DẠY THÊM TIẾNG ANH LỚP 7 CẢ NĂM FRIENDS PLUS SÁCH CHÂN TRỜI SÁNG TẠO ...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
What is Digital Literacy? A guest blog from Andy McLaughlin, University of Ab...
Gender and Mental Health - Counselling and Family Therapy Applications and In...
Gender and Mental Health - Counselling and Family Therapy Applications and In...
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
spot a liar (Haiqa 146).pptx Technical writhing and presentation skills
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
OCR
- 10. Why ANN?
- 15. ANN structure Neuron (nerve cell)
- 16. ANN structure
- 17. ANN structure
- 23. Thank you