GEOMETRIC CORRECTION FOR BRAILLE DOCUMENT IMAGEScsandit
Braille system has been used by the visually impaired people for reading.The shortage of Braille
books has caused a need for conversion of Braille to text. This paper addresses the geometric
correction of a Braille document images. Due to the standard measurement of the Braille cells,
identification of Braille characters could be achieved by simple cell overlapping procedure. The
standard measurement varies in a scaled document and fitting of the cells become difficult if the
document is tilted. This paper proposes a line fitting algorithm for identifying the tilt (skew)
angle. The horizontal and vertical scale factor is identified based on the ratio of distance
between characters to the distance between dots. These are used in geometric transformation
matrix for correction. Rotation correction is done prior to scale correction. This process aids in
increased accuracy. The results for various Braille documents are tabulated.
Nowadays character recognition has gained lot of attention in the field of pattern recognition due to its application in various fields. It is one of the most successful applications of automatic pattern recognition. Research in OCR is popular for its application potential in banks, post offices, office automation etc. HCR is useful in cheque processing in banks; almost all kind of form processing systems, handwritten postal address resolution and many more. This paper presents a simple and efficient approach for the implementation of OCR and translation of scanned images of printed text into machine-encoded text. It makes use of different image analysis phases followed by image detection via pre-processing and post-processing. This paper also describes scanning the entire document (same as the segmentation in our case) and recognizing individual characters from image irrespective of their position, size and various font styles and it deals with recognition of the symbols from English language, which is internationally accepted.
Optical character recognition, usually abbreviated to OCR, is the mechanical or electronic conversion of scanned images of handwritten, typewritten or printed text into machine-encoded text.
It is a system that provides a full alphanumeric recognition of printed or handwritten characters at electronic speed by simply scanning the form. It is widely used as a form of data entry from some sort of original paper data source, whether documents, sales receipts, mail, or any number of printed records.
It is a common method of digitizing printed texts so that they can be electronically searched, stored more compactly, displayed on-line, and used in machine processes such as machine translation, text-to-speech and text mining.OCR is a field of research in pattern recognition, artificial intelligence and computer vision. More recently, the term Intelligent Character Recognition(ICR) has been used to describe the process of interpreting image data, in particular alphanumeric text .
GEOMETRIC CORRECTION FOR BRAILLE DOCUMENT IMAGEScsandit
Braille system has been used by the visually impaired people for reading.The shortage of Braille
books has caused a need for conversion of Braille to text. This paper addresses the geometric
correction of a Braille document images. Due to the standard measurement of the Braille cells,
identification of Braille characters could be achieved by simple cell overlapping procedure. The
standard measurement varies in a scaled document and fitting of the cells become difficult if the
document is tilted. This paper proposes a line fitting algorithm for identifying the tilt (skew)
angle. The horizontal and vertical scale factor is identified based on the ratio of distance
between characters to the distance between dots. These are used in geometric transformation
matrix for correction. Rotation correction is done prior to scale correction. This process aids in
increased accuracy. The results for various Braille documents are tabulated.
Nowadays character recognition has gained lot of attention in the field of pattern recognition due to its application in various fields. It is one of the most successful applications of automatic pattern recognition. Research in OCR is popular for its application potential in banks, post offices, office automation etc. HCR is useful in cheque processing in banks; almost all kind of form processing systems, handwritten postal address resolution and many more. This paper presents a simple and efficient approach for the implementation of OCR and translation of scanned images of printed text into machine-encoded text. It makes use of different image analysis phases followed by image detection via pre-processing and post-processing. This paper also describes scanning the entire document (same as the segmentation in our case) and recognizing individual characters from image irrespective of their position, size and various font styles and it deals with recognition of the symbols from English language, which is internationally accepted.
Optical character recognition, usually abbreviated to OCR, is the mechanical or electronic conversion of scanned images of handwritten, typewritten or printed text into machine-encoded text.
It is a system that provides a full alphanumeric recognition of printed or handwritten characters at electronic speed by simply scanning the form. It is widely used as a form of data entry from some sort of original paper data source, whether documents, sales receipts, mail, or any number of printed records.
It is a common method of digitizing printed texts so that they can be electronically searched, stored more compactly, displayed on-line, and used in machine processes such as machine translation, text-to-speech and text mining.OCR is a field of research in pattern recognition, artificial intelligence and computer vision. More recently, the term Intelligent Character Recognition(ICR) has been used to describe the process of interpreting image data, in particular alphanumeric text .
A design of license plate recognition system using convolutional neural networkIJECEIAES
This paper proposes an improved Convolutional Neural Network (CNN) algorithm approach for license plate recognition system. The main contribution of this work is on the methodology to determine the best model for four-layered CNN architecture that has been used as the recognition method. This is achieved by validating the best parameters of the enhanced Stochastic Diagonal Levenberg Marquardt (SDLM) learning algorithm and network size of CNN. Several preprocessing algorithms such as Sobel operator edge detection, morphological operation and connected component analysis have been used to localize the license plate, isolate and segment the characters respectively before feeding the input to CNN. It is found that the proposed model is superior when subjected to multi-scaling and variations of input patterns. As a result, the license plate preprocessing stage achieved 74.7% accuracy and CNN recognition stage achieved 94.6% accuracy.
A Review of Optical Character Recognition System for Recognition of Printed Textiosrjce
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.
Free-scale Magnification for Single-Pixel-Width Alphabetic Typeface Charactersinventionjournals
This article presents a novel approach to magnify single-pixel-width alphabetic typeface characters. To this end, useless serif patterns of the character were first removed from the subsequent analysis to facilitate the zooming-in process and alleviate the computation burden. An intuitive algorithm for stroke transcribing was then advanced and applied to the serif-eliminated character. Finally, each stroke, which was represented by cubic B-Spline functions, was scaled by wavelet transform with arbitrary size. The proposed algorithm was validated on the computer fonts rendered with Roman, 12 point in Windows operation system. Experimental results demonstrate its good performance, which may provide convenient access to small devices and mobile interfaces for the future use.
Bangla Optical Digits Recognition using Edge Detection MethodIOSR Journals
Abstract:This paper is based on Bangla Optical Digit Recognition (ODR) by the Edge detection technique. In this method, Bangla digit image converted into gray-scale which distributed by an M by N array form. Here input data are considered off-line printed digit’s image which collected from computer generated image, scanned documents or printed text. After addressing the gray-scale image against a variable in the form of an M by N array, where the value of array pointers are shown 255 for total white space, 0 (zero) for total dark space and value between 255 and 0 for mix of white and dark space of the image. At the next process, four edgestouch points as well as each touch point’s ratio use as parameters to determine each Bangla digit uniquely. Keywords-Edge, image,gray-scale, Matrix,ODR.
Numeral recognition is an important research direction in field of pattern recognition, and it has
broad application prospects. Aiming at four arithmetic operations of general printed formats, this article
adopts a multiple hybrid recognition method and is applied to automatically calculating. This method mainly
uses BP neural network and template matching method to distinguish the numerals and operators, in order
to increase the operation speed and recognition accuracy. Sample images of four arithmetic operations are
extracted from printed books, and they are used for testing the performance of proposed recognition
method. The experiments show that the method provides correct recognition rate of 96% and correct
calculation rate of 89%.
Fingerprint Image Compression using Sparse Representation and Enhancement wit...Editor IJCATR
A technique for enhancing decompressed fingerprint image using Wiener2 filter is proposed. First compression is done by sparse representation. Compression of fingerprint is necessary for reducing the memory consumption and efficient transfer of fingerprint images. This is very essential for the application which includes access control and forensics. So the fingerprint image is compressed using sparse representation. In this technique, first dictionary is constructed for patches of fingerprint images. Then a fingerprint is selected and the coefficients are obtained and encoded. Thus the compressed fingerprint is obtained. But when the fingerprint is reconstructed, it is affected by noise. So Wiener2 filter is used to filter the noise in the image. The ridge and bifurcation count is extracted from decompressed and enhanced fingerprints. The experiment result shows that the enhanced fingerprint image preserves more bifurcation than decompressed fingerprint image. The future analysis can be considered for preserving ridges.
Image to Text Converter PPT. PPT contains step by step algorithms/methods to which we can convert images in to text , specially contains algorithms for images which contains human handwritting, can convert writting in to text, img to text.
PERFORMANCE EVALUATION OF JPEG IMAGE COMPRESSION USING SYMBOL REDUCTION TECHN...cscpconf
Lossy JPEG compression is a widely used compression technique. Normally the JPEG technique
uses two process quantization, which is lossy process and entropy encoding, which is considered
lossless process. In this paper, a new technique has been proposed by combining the JPEG
algorithm and Symbol Reduction Huffman technique for achieving more compression ratio. The
symbols reduction technique reduces the number of symbols by combining together to form a new
symbol. As a result of this technique the number of Huffman code to be generated also reduced.
The result shows that the performance of standard JPEG method can be improved by proposed method. This hybrid approach achieves about 20% more compression ratio than the Standard JPEG.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A design of license plate recognition system using convolutional neural networkIJECEIAES
This paper proposes an improved Convolutional Neural Network (CNN) algorithm approach for license plate recognition system. The main contribution of this work is on the methodology to determine the best model for four-layered CNN architecture that has been used as the recognition method. This is achieved by validating the best parameters of the enhanced Stochastic Diagonal Levenberg Marquardt (SDLM) learning algorithm and network size of CNN. Several preprocessing algorithms such as Sobel operator edge detection, morphological operation and connected component analysis have been used to localize the license plate, isolate and segment the characters respectively before feeding the input to CNN. It is found that the proposed model is superior when subjected to multi-scaling and variations of input patterns. As a result, the license plate preprocessing stage achieved 74.7% accuracy and CNN recognition stage achieved 94.6% accuracy.
A Review of Optical Character Recognition System for Recognition of Printed Textiosrjce
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.
Free-scale Magnification for Single-Pixel-Width Alphabetic Typeface Charactersinventionjournals
This article presents a novel approach to magnify single-pixel-width alphabetic typeface characters. To this end, useless serif patterns of the character were first removed from the subsequent analysis to facilitate the zooming-in process and alleviate the computation burden. An intuitive algorithm for stroke transcribing was then advanced and applied to the serif-eliminated character. Finally, each stroke, which was represented by cubic B-Spline functions, was scaled by wavelet transform with arbitrary size. The proposed algorithm was validated on the computer fonts rendered with Roman, 12 point in Windows operation system. Experimental results demonstrate its good performance, which may provide convenient access to small devices and mobile interfaces for the future use.
Bangla Optical Digits Recognition using Edge Detection MethodIOSR Journals
Abstract:This paper is based on Bangla Optical Digit Recognition (ODR) by the Edge detection technique. In this method, Bangla digit image converted into gray-scale which distributed by an M by N array form. Here input data are considered off-line printed digit’s image which collected from computer generated image, scanned documents or printed text. After addressing the gray-scale image against a variable in the form of an M by N array, where the value of array pointers are shown 255 for total white space, 0 (zero) for total dark space and value between 255 and 0 for mix of white and dark space of the image. At the next process, four edgestouch points as well as each touch point’s ratio use as parameters to determine each Bangla digit uniquely. Keywords-Edge, image,gray-scale, Matrix,ODR.
Numeral recognition is an important research direction in field of pattern recognition, and it has
broad application prospects. Aiming at four arithmetic operations of general printed formats, this article
adopts a multiple hybrid recognition method and is applied to automatically calculating. This method mainly
uses BP neural network and template matching method to distinguish the numerals and operators, in order
to increase the operation speed and recognition accuracy. Sample images of four arithmetic operations are
extracted from printed books, and they are used for testing the performance of proposed recognition
method. The experiments show that the method provides correct recognition rate of 96% and correct
calculation rate of 89%.
Fingerprint Image Compression using Sparse Representation and Enhancement wit...Editor IJCATR
A technique for enhancing decompressed fingerprint image using Wiener2 filter is proposed. First compression is done by sparse representation. Compression of fingerprint is necessary for reducing the memory consumption and efficient transfer of fingerprint images. This is very essential for the application which includes access control and forensics. So the fingerprint image is compressed using sparse representation. In this technique, first dictionary is constructed for patches of fingerprint images. Then a fingerprint is selected and the coefficients are obtained and encoded. Thus the compressed fingerprint is obtained. But when the fingerprint is reconstructed, it is affected by noise. So Wiener2 filter is used to filter the noise in the image. The ridge and bifurcation count is extracted from decompressed and enhanced fingerprints. The experiment result shows that the enhanced fingerprint image preserves more bifurcation than decompressed fingerprint image. The future analysis can be considered for preserving ridges.
Image to Text Converter PPT. PPT contains step by step algorithms/methods to which we can convert images in to text , specially contains algorithms for images which contains human handwritting, can convert writting in to text, img to text.
PERFORMANCE EVALUATION OF JPEG IMAGE COMPRESSION USING SYMBOL REDUCTION TECHN...cscpconf
Lossy JPEG compression is a widely used compression technique. Normally the JPEG technique
uses two process quantization, which is lossy process and entropy encoding, which is considered
lossless process. In this paper, a new technique has been proposed by combining the JPEG
algorithm and Symbol Reduction Huffman technique for achieving more compression ratio. The
symbols reduction technique reduces the number of symbols by combining together to form a new
symbol. As a result of this technique the number of Huffman code to be generated also reduced.
The result shows that the performance of standard JPEG method can be improved by proposed method. This hybrid approach achieves about 20% more compression ratio than the Standard JPEG.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Tracking number plate from vehicle usingijfcstjournal
In Traffic surveillance, Tracking of the number plate from the vehicle is an important task, which demands
intelligent solution. In this document, extraction and Recognization of number plate from vehicles image
has been done using Matlab. It is assumed that images of the vehicle have been captured from Digital
Camera. Alphanumeric Characters on plate has been Extracted and recognized using template images of
alphanumeric characters.
This paper presents a new algorithm in MATLAB which has been used to extract the number plate from the
vehicle in various luminance conditions. Extracted image of the number plate can be seen in a text file for
verification purpose. Number plate identification is helpful in finding stolen cars, car parking management
system and identification of vehicle in traffic.
Authentication of a person is the major concern in this era for security purposes. In biometric systems Signature is one of the behavioural features used for the authentication purpose. In this paper we work on the offline signature collected through different persons. Morphological operations are applied on these signature images with Hough transform to determine regular shape which assists in authentication process. The values extracted from this Hough space is used in the feed forward neural network which is trained using back-propagation algorithm. After the different training stages efficiency found above more than 95%. Application of this system will be in the security concerned fields, in the defence security, biometric authentication, as biometric computer protection or as method of the analysis of person’s behaviour changes.
License Plate Recognition using Morphological Operation. Amitava Choudhury
This paper describes an efficient technique of locating and
extracting license plate and recognizing each segmented
character. The proposed model can be subdivided into four
parts- Digitization of image, Edge Detection, Separation of
characters and Template Matching. In this work, we propose a
method which is based on morphological operations where
different Structuring Elements (SE) are used to maximally
eliminate non-plate region and enhance plate region.
Character segmentation is done using Connected Component
Analysis. Correlation based template matching technique is
used for recognition of characters. This system is
implemented using MATLAB7.4.0. The proposed system is
mainly applicable to Indian License Plates.
Offline signature identification using high intensity variations and cross ov...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Segmentation and recognition of handwritten digit numeral string using a mult...ijfcstjournal
In this paper, the use of Multi-Layer Perceptron (MLP) Neural Network model is proposed for recognizing
unconstrained offline handwritten Numeral strings. The Numeral strings are segmented and isolated
numerals are obtained using a connected component labeling (CCL) algorithm approach. The structural
part of the models has been modeled using a Multilayer Perceptron Neural Network. This paper also
presents a new technique to remove slope and slant from handwritten numeral string and to normalize the
size of text images and classify with supervised learning methods. Experimental results on a database of
102 numeral string patterns written by 3 different people show that a recognition rate of 99.7% is obtained
on independent digits contained in the numeral string of digits includes both the skewed and slant data.
Two Methods for Recognition of Hand Written Farsi CharactersCSCJournals
Optical character recognition (OCR) is one of the active bases of sample detection topics. The current study focuses on automatic detection and recognition of hand written Farsi characters. For this purpose; we proposed two different methods based on neural networks and a special post processing approach to improve recognition rate of Farsi uppercase letters. In the first method, we extracted wavelet features from borders of character images and learned a neural network based these patterns. In the second method, we divided input characters into five groups according to the number of their components and used a set of appropriate moment features in each group and classified characters by the Bayesian rule. In a post-processing stage, some structural and statistical features were employed by a decision tree classifier to reduce the misrecognition rate. Our experimental results show suitable recognition rate for both methods.
14 offline signature verification based on euclidean distance using support v...INFOGAIN PUBLICATION
In this project, a support vector machine is developed for identity verification of offline signature based on the matrices derived through Euclidean distance. A set of signature samples are collected from 35 different people. Each person gives his 15 different copies of signature and then these signature samples are scanned to have softcopy of them to train SVM. These scanned signature images are then subjected to a number of image enhancement operations like binarization, complementation, filtering, thinning, edge detection and rotation. On the basis of 15 original signature copies from each individual, Euclidean distance is calculated. And every tested image is compared with the range of Euclidean distance. The values from the ED are fed to the support vector machine which draws a hyper plane and classifies the signature into original or forged based on a particular feature value.
Mobile Based Application to Scan the Number Plate and To Verify the Owner Det...inventionjournals
Any License plate recognition system usually passes through three steps of image processing: 1) Extraction of a license plate region; 2) Segmentation of the plate characters; and 3) Recognition of each character. A number of algorithms have been proposed in recent times for efficient disposal of the application. The purpose of this project was to develop a real time application which recognizes number plates from cars at a gate, for example at the entrance of a parking area or a border crossing. The system, based on regular PC with mobile camera, catches video frames which include a visible car number plate and processes them. Once a number plate is detected, its digits are recognized, displayed on the User Interface or checked against a database.The software aspect of the system runs on mobile hardware and can be linked to other applications or databases. It first uses a series of image manipulation techniques to detect, normalize and enhance the Image of the number plate, and then optical character recognition (ocr) to extract the alpha numeric text of number plate. The system are generally deployed in one of two basic approaches: one allows for the entire process to be performed at the lane location in real-time. The other will reveal the driver’s profile by checking in the registered database.
Offline signatures matching using haar wavelet subbandsTELKOMNIKA JOURNAL
The complexity of multimedia contents is significantly increasing in the current world. This leads to an exigent demand for developing highly effective systems tosatisfy human needs. Until today, handwritten signature considered an important means that is used in banks and businesses to evidence identity, so there are many works triedto develop a method for recognition purpose. This paper introduced an efficient technique for offline signature recognition depending on extracting the local feature by utilizing the haar wavelet subbands and energy. Three different setsof features are utilized by partitioning the signature image into non overlapping blocks where different block sizes are used. CEDAR signature database is used asa dataset for testing purpose. The results achieved by this technique indicate a high performance in signature recognition.
Reconstructing the Path of the Object based on Time and Date OCR in Surveilla...ijtsrd
The inclusion of time based queries in video indexing application is enables by the recognition of time and date stamps in CCTV video. In this paper, we propose the system for reconstructing the path of the object in surveillance cameras based on time and date optical character recognition system. Since there is no boundary in region for time and date, Discrete Cosine Transform DCT method is applied in order to locate the region area. After the region for time and date is located, it is segmented and then features for the symbols of the time and date are extracted. Back propagation neural network is used for recognition of the features and then stores the result in the database. By using the resulted database, the system reconstructs the path for the object based on time. The proposed system will be implemented in MATLAB. Pyae Phyo Thu | Mie Mie Tin | Ei Phyu Win | Cho Thet Mon "Reconstructing the Path of the Object based on Time and Date OCR in Surveillance System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27981.pdfPaper URL: https://www.ijtsrd.com/home-science/education/27981/reconstructing-the-path-of-the-object-based-on-time-and-date-ocr-in-surveillance-system/pyae-phyo-thu
A Novel Framework For Numerical Character Recognition With Zoning Distance Fe...IJERD Editor
Advancements of Computer technology has made every organization to implement the automatic processing systems for its activities. One of the examples is the recognition of handwritten characters, which has always been a challenging task in image processing and pattern recognition. In this paper we propose Zone based features for recognition of the handwritten characters. In this zoning approach a digit image is divided into 8x8 zones and centre pixel is computed for each zone. This procedure is sequentially repeated for entire zone. Finally features are extracted for classification and recognition.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
When stars align: studies in data quality, knowledge graphs, and machine lear...
K012647982
1. IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE)
e-ISSN: 2278-1684,p-ISSN: 2320-334X, Volume 12, Issue 6 Ver. IV (Nov. - Dec. 2015), PP 79-82
www.iosrjournals.org
DOI: 10.9790/1684-12647982 www.iosrjournals.org 79 | Page
A Simple Signature Recognition System
Suvarnsing G. Bhable
Research Student Dept. of CS & IT Dr. B.A.M.University, Aurangabad
Abstract: The signature of a person is an important biometric characteristic of a human being which can be
used to verify human identity. Signature verification is an important research area in the field of authentication
of a person as well as documents in e-commerce and banking. Signatures are verified based on features
extracted from the signature using Invariant Central Moment and Modified Zernike moment for its invariant
feature extraction because the signatures are Hampered by the large amount of variation in size, translation
and rotation and shearing parameter. This signature recognition system is designed using MATLAB. This work
has been tested and found suitable for its purpose.
Keywords: Biometrics, Hidden Markov models (HMM), Normalized area of signature, Off-line Signature
Recognition, OCR
I. Introduction
Biometrics is technologies used for measuring and analysing a person's unique characteristics. There
are two types of biometrics: behavioral and physical. Behavioral biometrics are generally used for verification
while physical biometrics can be used for either identification or verification. Among the different forms of
biometric recognition systems such as fingerprint, iris, DNA, face, voice, vein structure palm etc.,In our society,
traditional and accepted means for a person to identify and authenticate himself either to another human being or
to a computer system is based on one or more of these three (3) general principles:
What the person knows
What he possesses
What he is
The signature recognition & verification system shown in Fig. 1 is broadly divided into three subparts
a) Preprocessing,
b) Feature extraction,
c) Recognition & Verification.
The input signature is captured from the scanner or digital high pixel camera which provides the output
image in term of BMP Color image. The preprocessing algorithm provides the required data suitable for the
final processing. In the feature extraction phase the invariant central moment and Zernike moment are used to
extract the feature for the classification purpose. In classification the Back propagation Neural Network is used
to provide high accuracy and less computational complexity in training and testing phase of the system.
Fig.1: Flow Diagram of SRS (Signature Recognition System)
2. A Simple Signature Recognition System
DOI: 10.9790/1684-12647982 www.iosrjournals.org 80 | Page
II. Classification
Major techniques used for offline signature verification system are based on Template Matching,
Statistical Approach, Structural Analysis Approach, Spectrum Analysis Approach, Neural Network Approach
[1]
Template Matching Approach – The template matching is the simplest and earliest but rigid
approach to pattern recognition in which instances of pre-stored patterns are sought in an image. It is
performed at the pixel level and also on higher level. This approach has a number of disadvantages due
to its rigidity. It may fail if the patterns are distorted due to the imaging process, viewpoint change etc
as in the case of signatures. It can detect casual forgeries from genuine signatures But cant verify
between the genuine signature and skilled ones. The template matching method can be categorized into
several forms such as graphics matching, stroke analysis and geometric feature extraction, depending
on different features.
Statistical Approach – In this approach, each pattern
is represented in terms of features and is viewed as a
point in a d-dimensional space. Each pattern vector
belonging to different categories occupy compact and disjoint regions in a d-dimensional feature space.
Decision boundaries are set in feature space to separate different classes. The effectiveness of the
feature set is determined by how well patterns from different classes can be separated. Hidden Markov
Model (HMM), Bayesian these are some statistical approach commonly used in pattern recognition.
They can detect causal forgeries as well as skilled and traced forgeries from the genuine ones.
Structural Approach - It is related to graph, string and tree matching techniques and is used in
combination with other techniques. It shows good performance detecting genuine signatures and
forgeries. Its major disadvantage is that it uses large dataset for greater accuracy.
Spectrum Analysis Approach- In this method the first stage of the procedure is the transformation of
the data into another matrix which is a version of the trajectory matrix in Spectrum Analysis. Than a
square window is placed in all possible places of image.[2] It basically decomposes a curvature-based
signature into a multi-resolution format. This approach is used for long scripted signatures
Neural Network Approach- The main characteristics of neural networks are that they have the ability
to learn complex nonlinear input-output relationships, use sequential training procedures, and adapt
themselves to the data. The most commonly used family of neural networks for pattern classification
tasks is the feed-forward network, which includes multilayer perceptron, Radial-Basis Function (RBF)
networks Self-Organizing Map (SOM), or Kohonen- Network.
III. Database
For training and testing of the signature recognition and verification system 500 signatures are used.
The signatures were taken from 50 persons. The templates of the signature as shown in Fig.2 for training the
system 50 person’s signatures are used. Each of these persons signed 8 original signature and The input
signature is captured from the scanner or digital high pixel camera which provides the output image in term of
BMP Colour image. The preprocessing algorithm provides the required data suitable for the final processing. In
the feature extraction phase the invariant central moment and Zernike moment are used to extract the feature for
the classification purpose. In classification the Back propagation Neural Network is used to provide high
accuracy and less computational complexity in training and testing phase of the system.
Signed 4 forgery signatures in the training set the total number of signatures is 500 (10 x 50) are used.
In order to make the system robust, signers were asked to use as much as variation in their signature size and
shape and the signatures are collected at different times without seeing other signatures they signed before.
For testing the system, another 100 genuine signatures and 100 forgery signatures are taken from the same 50
persons in the training set.
IV. Preprocessing
Signature verification system is pre-processing. The need of pre-processing is explained through
system. It can be clearly seen that while scanning a signature on white paper, residues are also scanned. This
increases in fuzziness of the pattern. Thus, in the first step such scanning noise must be eliminated. We perform
this by first converting the image to gray scale image. We then convert the image to binary image with a
threshold. This results in particularly clear signature pattern as seen in system. Considering that the signature is
scanned against a white background, it can be present at any side of the paper or it can be centralized.
Considering this whole image as a sample will lead to improper statistics. Hence ROI of the signature must be
3. A Simple Signature Recognition System
DOI: 10.9790/1684-12647982 www.iosrjournals.org 81 | Page
extracted first. Many literatures have proposed signature ROI detection using simple bounding box which is
demonstrated in system. This involves two steps: First inverting the signature so that background becomes black
and foreground is white and then obtaining a bounding box. However this traditional solution is many
limitations when it comes to detecting discontinues signature as shown in that system.
This problem is overcome by first dilating input signature with a structuring element of size 16x16 and
then applying the bounding box over it. The bounding box region is then annotated over non dilated image to
extract the exact region. Results are shown in system.
Process of binary conversion also results in disconnected lines due to conversion error. This is
overcome by first dilating and then eroding the binary image.
V. Feature Extraction
Emphasis of the proposed work is in detecting shape or boundary features. Features can be applied on
binary image or thin image or edges. We performed a test to analyse the dominance of features in all three
scenarios.
Figure 6 reveals that radon features are natural to shapes. Thus descriptors are dominant in same
dimension for both normal as well as edge detected images. However number of dominant features is low in
both cases. In order to obtain better descriptors we applied thinning with a structuring of kernel 2x2. Results are
presented in Figure 7.
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Therefore it is proved that region of interest extraction must be followed by thinning process to extract
good descriptor in signature verification system. Size is another important aspect of signatures. Local feature
extraction techniques like Grid/Zone based features extractors demand that all the images be of same size. To
study the effect of resizing, we performed feature extraction from resized ROI and without resizing. It can be
clearly seen from that resizing induces interpolation losses. Hence feature descriptor changes. This leads to
misclassification and results in low accuracy. To avoid this problem descriptor must be used on the actual image
rather than resized image. However actual image size will vary from one signature to the other. Thus number of
descriptors will also vary. One of the prerequisite for any classification is that feature dimensions must be same.
Therefore it is wise to extract projection on different angles and extract statistics from them. It can be clearly
seen that the transform descriptors varies as angle of projection varies. It is quite difficult to claim the actual
projections that would result in optimized feature set. Hence we obtain Radon descriptors for 0' to 360' in steps
of 15' and extract mean and standard deviation for each projection as our Radon feature set. Once Radon
transform is extracted, we obtain Zernike moments before classifying or adding the features to database. Zernike
like Radon is a shift invariant moments obtained from polar projection of image. Zernike moments are complex.
Therefore real components from the moments are extracted as feature descriptor. Another major limitation of
using Zernike moment is that the exponent of the dimension increases as number of moments is increased. But
for modelling with HMM, dimensions must be normalized to single value domain. Hence after obtaining
Zernike moments we normalize each dimension by dividing it with the highest exponent of that dimension. Thus
all the feature values are brought in same value domain. Overall methodology is explained with a block diagram
VI. Conclusion
Signature verification and analysis are part of larger domain of work which finds application in
graphology and forensic science. In this work we have presented a Novel technique of Signature Verification by
combining Zernike moments with Radon transform values at different angle of projection from the user's
Signature pattern and then forming a statistical state machine with HMM and PLSR. Further the technique was
improved by the aid of kernel based techniques with the Help of SVM. It gives the poor performance for
signature that is not in the training phase. Generally the failure to recognize/verify a signature was due to poor
image quality and high similarity between 2 signatures. Recognition and verification ability of the system can be
increased by using additional features in the input data set.
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