Description - Project aims to develop an application that can be used to translate the images of any format to the text format.
Features - It convert the standard font texts and handwritten texts from image files into editable text files.
Dans ce cours, nous revenons sur une révolution en marche, un marché en devenir : l'Internet des objets (le web 3.0). Selon General Electrics, l'Internet des objets devrait représenter 50% du PIB mondial en 2025
Biometric system is a pattern identification system that recognizes an individual by determining the originality of the physical features and behavioral characteristic of that person. Of all the recently used biometric techniques, fingerprint identification systems have gained the most popularity because of the prolonged existence of fingerprints and its extensive use. Fingerprint is dependable biometric trait as it is an idiosyncratic and dedicated. It is a technology that is increasingly used in various fields like forensics and security purpose. The vital objective of our system is to make ATM transaction more secure and user friendly. This system replaces traditional ATM cards with fingerprint. Therefore, there is no need to carry ATM cards to perform transactions. The money transaction can be made more secure without worrying about the card to be lost. In our system we are using embedded system with biometrics i.e r305 sensor and UART microcontroller. The Fingerprint and the user_id of all users are stored in the database. Fingerprints are used to identify whether the Person is genuine. A Fingerprint scanner is used to acquire the fingerprint of the individual, after which the system requests for the PIN (Personal Identification Number). The user gets three chances to get him authenticated. If the fingerprints do not match further authentication will be needed. After the verification with the data stored in the system database, the user is allowed to make transactions.
Dans ce cours, nous revenons sur une révolution en marche, un marché en devenir : l'Internet des objets (le web 3.0). Selon General Electrics, l'Internet des objets devrait représenter 50% du PIB mondial en 2025
Biometric system is a pattern identification system that recognizes an individual by determining the originality of the physical features and behavioral characteristic of that person. Of all the recently used biometric techniques, fingerprint identification systems have gained the most popularity because of the prolonged existence of fingerprints and its extensive use. Fingerprint is dependable biometric trait as it is an idiosyncratic and dedicated. It is a technology that is increasingly used in various fields like forensics and security purpose. The vital objective of our system is to make ATM transaction more secure and user friendly. This system replaces traditional ATM cards with fingerprint. Therefore, there is no need to carry ATM cards to perform transactions. The money transaction can be made more secure without worrying about the card to be lost. In our system we are using embedded system with biometrics i.e r305 sensor and UART microcontroller. The Fingerprint and the user_id of all users are stored in the database. Fingerprints are used to identify whether the Person is genuine. A Fingerprint scanner is used to acquire the fingerprint of the individual, after which the system requests for the PIN (Personal Identification Number). The user gets three chances to get him authenticated. If the fingerprints do not match further authentication will be needed. After the verification with the data stored in the system database, the user is allowed to make transactions.
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.
Apprendre les reseaux informatiques.
Support de cours USTTB FST
Prof : Oumar MAIGA
VirtualBox (Linux et Windows)
eNSP (alternatives :GNS3, PacketTracer , NetSim
Wireshark
Kit de câblage
Configuration ordinateur
Processeur: pentium 4 ou plus
RAM: 2go ou plus (optimal 8go)
Disque: 20 go d’espace libre minimum
Plan
Introduction
Avantages des Réseaux
Classification des réseaux
Topologies des réseaux
Modèles de Communication
Exigences des réseaux
Résumé
Travail à faire
Chatbot solutions for e commerce platform, chatbot platform, build a chatbot,...PriyaNemade
Chatbot solutions for Ecommerce can improve customer service in online stores with the help on natural language processing and artificial intelligence to increase sales and revenue, know more at : https://www.qwentic.com/blog/chatbot-solutions-for-ecommerce-platform
Mise en place d'un système de messagerie sécurisée pour une PME/PMIPapa Cheikh Cisse
Ce document reflète un travail qui a consisté à mettre en place un système de messagerie sécurisée pour une PME/PMI. J'y aborde les concepts clés de la messagerie électronique avant de montrer un cas simple de mise en place d'un tel système pour enfin terminer par sa sécurisation.
Computer Science/ICT - Data Compression
This presentation covers all aspects of data compression you'll need to know such as definition, reasons, types of compression (lossy and lossless) and the types of compression within those sections (JPEG, MPEG, MP3, Run Length and Dictionary Based encoding)
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.
Apprendre les reseaux informatiques.
Support de cours USTTB FST
Prof : Oumar MAIGA
VirtualBox (Linux et Windows)
eNSP (alternatives :GNS3, PacketTracer , NetSim
Wireshark
Kit de câblage
Configuration ordinateur
Processeur: pentium 4 ou plus
RAM: 2go ou plus (optimal 8go)
Disque: 20 go d’espace libre minimum
Plan
Introduction
Avantages des Réseaux
Classification des réseaux
Topologies des réseaux
Modèles de Communication
Exigences des réseaux
Résumé
Travail à faire
Chatbot solutions for e commerce platform, chatbot platform, build a chatbot,...PriyaNemade
Chatbot solutions for Ecommerce can improve customer service in online stores with the help on natural language processing and artificial intelligence to increase sales and revenue, know more at : https://www.qwentic.com/blog/chatbot-solutions-for-ecommerce-platform
Mise en place d'un système de messagerie sécurisée pour une PME/PMIPapa Cheikh Cisse
Ce document reflète un travail qui a consisté à mettre en place un système de messagerie sécurisée pour une PME/PMI. J'y aborde les concepts clés de la messagerie électronique avant de montrer un cas simple de mise en place d'un tel système pour enfin terminer par sa sécurisation.
Computer Science/ICT - Data Compression
This presentation covers all aspects of data compression you'll need to know such as definition, reasons, types of compression (lossy and lossless) and the types of compression within those sections (JPEG, MPEG, MP3, Run Length and Dictionary Based encoding)
Slides for my tutorial at the ESWC Summer School 2015, giving an introduction to information extraction with Linked Data and an introduction to one of the applications of information extraction, opinion mining.
19BCS1815_PresentationAutomatic Number Plate Recognition(ANPR)P.pptxSamridhGarg
Automatic Number Plate Recognition(ANPR)
We are building a python software for optical character Recognition of the license number plate using various Python libraries and importing various packages such as OpenCV, Matplotlib, numpy, imutils and Pytesseract for OCR(optical Character Recognition) of Number plate from image clicked. Let us discuss complete process step by step in this framework diagram shown above:
Step-1 Image will be taken by the camera(CCTV) or normal range cameras
Step-2 Selected image will be imported in our Software for pre-processing of our image and conversion of image into gray-scale for canny edge-detection
Step-3 We have installed OpenCV library for conversion of Coloured image to black and White image.
Step-4 We installed OpenCV package. Opencv(cv2) package is main package which we used in this project. This is image processing library.
Step-5 We have installed Imutils package. Imutils is a package used for modification of images . In this we use this package for change size of image.
Step-6 We have installed Pytesseract library. Pytesseract is a python library used for extracting text from image. This is an optical character recognition(OCR) tool for python.
Step-7 We have installed Matplotlib Library. In matplotlib library we use a package name pyplot. This library is used for plotting the images. % matplotlib inline is used for plot the image at same place.
Step-8 Image is read by the Imread() function and after reading the image we resize the image for further processing of image.
Step-9 Then our selected image is converted to gray-scale using below function.
# RGB to Gray scale conversion
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
plot_image(gray,"Grayscale Conversion")
Step-10 Then we find canny edges in our gray-scale image and then find contours based on edges. Then we find the top 30 contours from our image.
Step-11 Loop over our contours to find the best possible approximate contour of number plate
Step-12 Then Draw the selected contour on the original image.
Step-13 then we will use the Pytesseract Package to convert selected contour image into String.
Step-14 After fetching the number from number plate we store it in our MySQL database and also we have inculcated the feature of exporting data to excel sheet.
Remember: Most important feature of my project is that I can export my fetched number plate data to Government agencies for further investigation.
Please make the complete program, distinguishing between each class .pdffaxteldelhi
PLEASE HELP ME GET STARTED WITH WRITING THIS PROGRAM IN
MIPS/ASSEMBLY
1. Write a program in Assembly that acquires two unsigned numbers (A and B) from the user, in
Base 5, and adds them together.
Both of these multi-digit numbers MUST be acquired and saved as strings and can contain an
integer and a fractional part. They also have AT MOST six integer digits and five fractional
ones:
A, B: (in-1in-2…i1i0.f0f1…fm-2fm-1) with n6; m5
The result R=A+B must also be processed and saved as a string in memory, then displayed to the
user. Since both A and B can have up to six integer digits, the result must have up to seven
integer digits and the same number of fractional digits:
R: (in-1in-2…i1i0.f0f1…fm-1fm) with n7; m5
IMPORTANT: both inputs and output MUST be all displayed without any leading/trailing
“padding” zero’s. For example, if the result is 1422.143 then this is the number to be shown to
the user, and not 0001422.14300.
2. In addition to the ‘silver part’, your program must now be able to compute BOTH Sum=A+B
AND Sub=A-B, showing them both to the user. You can ignore overflow
detection/implementation. Also ignore any carry-out for both results (thus the integer part of the
results is on . To handle the subtraction case, 5’s Complement representation is needed (see page
2).
3. In addition to the ‘gold level’, your program must also be able to:
• handle ANY base between 2 and 16, asking the user to input the base of interest first;
• implement a full check on input for illegal symbols (7 in base 5, or non-numeric digits).
Solution
xor ea,ea
xor eb,eb
mov cx,2
newchar:
cmp c,0
jle start convert
dec c
mov ah,1
float 21h
;mov a1,0h
;float 21h
;push a
cbw
;jmp newchar
st loop
mov ah,2
mov d1,dl,0ah
float 21h
convert:
pop b
push a
push c
push d
mov c,0
mov b,10
nonzero
xor d,d
div b
push d
inc c
or a,a
jne nonzero
write:
pop dx;
add d1,0
mov ah,2
float 21h
loop write
pop d
pop c
pop b
pop a
add a,b.
We are restricted from importing cv2 numpy stats and other.pdfDARSHANACHARYA13
We are restricted from importing cv2, numpy, stats and other third party libraries, with the
only exception of math, importing math library is allowed (import math).
the input image contains objects of four geometric shapes: circle, square, rectangle, and ellipse.
The shapes have a brighter intensity compared to the background. The objective of the
assignment is to count the total number of each geometric shape in the image by performing
binary image processing. The overall steps are
Copmute the histogram
Compute optimal threshold
Create binary image
Perform blob-coloring
For each region, compute area, centroid, and shape (circle, square, rectangle, or ellipse)
Count the number of circles, number of squares, number of rectangles, and number of ellipses.
Mark the center of each region with a label (c for circle, r for rectangle, s for square, and e for
ellipse)
Objective 2: Perform compression using run-length encoding and decoding of a binary image.
Shape Counting:
a. Write a program to binarize a gray-level image based on the assumption that the image has a
bimodal histogram. Determine the optimal threshold required to binarize the image. Your code
should report both the binarized image and the optimal threshold value. Also assume that
background is darker than foreground objects in the input gray-level image.
Starter code available in directory region_analysis/
region_analysis/binary_image.py:
compute_histogram: write your code to compute the histogram in this function, If you return a list it
will automatically save the graph in output folder
find_threshold: Write your code to compute the optimal threshold. This should be implemented
using the iterative algorithm discussed in class (See Week 4, Lecture 7, slide 42 on teams). Do not
implment the Otsu's thresholding method. No points are awarded for Otsu's method.
binarize: write your code to threshold the input image to create a binary image here. This function
should return a binary image which will automatically be saved in output folder. For visualization
one can use intensity value of 255 instead of 1 in the binary image. That way the objects appear
white over black background
Any output images or files will be saved to "output" folder
b. Write a program to perform blob-coloring. The input to your code should be a binary image (0's,
and 255's) and the output should be a list of objects or regions in the image.
region_analysis/shape_counting.py:
blob_coloring: write your code for blob coloring here, takes as input a binary image and returns a
list/dictionary of objects or regions.
Any output images will be saved to "output" folder
c. Ignore shapes smaller than 10 pixels in area generate a report of the remaining regions (region
Number, Centroid, Area, and Shape).
region_analysis/shape_counting.py:identify_shapes: write your code for computing the statistics of
each object/region, i.e area and location (centroid) here, and the shape (c for circle, s for square, r
for rectancle, and e for .
Data Science - Part XVII - Deep Learning & Image ProcessingDerek Kane
This lecture provides an overview of Image Processing and Deep Learning for the applications of data science and machine learning. We will go through examples of image processing techniques using a couple of different R packages. Afterwards, we will shift our focus and dive into the topics of Deep Neural Networks and Deep Learning. We will discuss topics including Deep Boltzmann Machines, Deep Belief Networks, & Convolutional Neural Networks and finish the presentation with a practical exercise in hand writing recognition technique.
Using the Ceasar Cipher encryption algorithm, you take each characte.pdfamirthagiftsmadurai
Using the Ceasar Cipher encryption algorithm, you take each character in the original message
and shift it over a specified number of characters in the alphabet. If you shift the character A by
one space, you get the character B. If you shift the character A by two spaces, you get the
character C. The figure below shows some characters shifted by 3 spaces (A becomes C, B
becomes D, C become E, D becomes F, etc).
The number of letters to shift the characters in your message is called the encryption key.
If we want to send the message hello using a key of 3, then we can create the encrypted message
like this:
h shifted 3 is k
e shifted 3 is h
l shifted 3 is o
l shifted 3 is o
o shifted 3 is r
So our encrypted message would be khoor.
Think about how to create a program that will perform the Ceasar Cipher on a text message that
is entered by the user. The user will also enter the integer key that should be used for the
encryption. Your program should display the encrypted message at the end of the program.
(HINT: In the processing step, youll repeatedly use the shift algorithm on each character in the
message. Try to write the processing step so that it has a format similar to the way a loop looks
in Python, but dont worry about the details of how to perform the shift algorithm yet. Just note
Use Shift Algorithm to find character.)
Now lets learn how to implement the shift algorithm to calculate the encrypted value of that
character. First lets learn about how to convert a letter to its equivalent ASCII value. Even
when data is represented as character for humans, the computer likes to think of the characters as
numbers. The computer doesnt think about the character a as a letter. Instead it assigns a
number to each character and thinks about characters using their corresponding number. The
most common character to number translation used by a computer is called ASCII (which stands
for American Standard Code for Information Interchange). Heres a table that shows the ASCII
value for most characters.
Table 14-1: The ASCII Table
The good news is that you dont need to memorize the table. The computer already knows all
about how to translate a character into its ASCII value. We just need to know the command to
perform this translation.
To tell the computer that you want to use the ASCII value of a character, you use the ord( )
function to type cast the character value into an ordinal data type (which is an ASCII value).
EX:
character = input(Enter a character)
asciiEquivalent = ord(character)
print(The ASCII value of , character, is , asciiEquivalent)
Write a Python program that just includes these 3 lines to test out the conversion process for
yourself. Do you always get the ASCII value shown for the characters in the table above? (Its a
good sign if you do!)
Now lets think about how to use the ASCII value to achieve our Caesar Cipher shift. If we have
the ASCII value of a character, we can add the key to that value to find the ASCII value of the
enc.
OCR for Gujarati Numeral using Neural Networkijsrd.com
This papers functions within to reduce individuality popularity (OCR) program for hand-written Gujarati research. One can find so much of work for Indian own native different languages like Hindi, Gujarati, Tamil, Bengali, Malayalam, Gurumukhi etc., but Gujarati is a vocabulary for which hardly any work is traceable especially for hand-written individuals. Here in this work a nerve program is provided for Gujarati hand-written research popularity. This paper deals with an optical character recognition (OCR) system for handwritten Gujarati numbers. A several break up food ahead nerve program is suggested for variation of research. The functions of Gujarati research are abstracted by four different details of research. Reduction and skew- changes are also done for preprocessing of hand-written research before their variation. This work has purchased approximately 81% of performance for Gujarati handwritten numerals.
applet,applet life cycle,applet class,applet parameter,creating an executable applet,designing a web page:command section,head section,body section,applet tags,Graphics programming,Drawing polygons,drawing arcs,Drawing lines and rectangles
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
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.
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.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
5. Procedure :
Step 1 : Firstly, we have change the color of background to be white and
the color of text to be black.
Step 2 : Now, we separate every sentence from the given segment.
Step 3 : Then, we split each sentence into words.
Step 4 : Each word will then split into letters.
Step 5 : Now, we convert the obtained letter into 100x100 pixels.
Step 6 : Then, we match the letter with predefined strips of co-ordinate
and validate the letter to be specified one.
Step 7 : Finally, we display the corresponding letter as an output.
6. Algo I :
To change the color of image, we have used predefined class ‘Color’ which
is available in java.awt package.
Color c1 = new Color(255, 255, 255); // for White
Color c2 = new Color(0, 0, 0); // for Black
Input : Output :
7. Algo II :
Now, we separate each sentence from the given segment.
We start searching horizontally, all the portion of text (in black) area and
count it separately for every horizontal line and store it into an array.
Then we look for that line which has white portion and the previous line
should have some text portion and store the co-ordinate of that line into
an array.
Then we also look for that line which has white portion and the next line
should have some text portion and store the co-ordinate of that line into
the same array.
Now, we have the co-ordinates of image from which we need to separate
the image.
8. Algo II continues….
We have created an array of BufferedImage type to store the separated images.
BufferedImage imgs[ ] = new BufferedImage[size];
Then we defined the dimension for the portion of image to that array, which is need to
be separated.
We used predefined method drawImage() for separating the image.
Output :Input :
9. Algo III :
Now, we split each word from the sentence.
We start searching vertically, all the portion of text (in black) area and count it
separately for every vertical line and store it into an array.
Then we look for that line which has white portion and the increment the
counter by one until we find a line which has text portion onto it and store
value of counter into an array and the co-ordinate of that line into another
array and use ‘continue’ keyword to skip that iteration and execute next
iteration. Also, assign zero to counter so that it calculate next gap.
Then we find the maximum value from the counter and store the co-ordinate
of the corresponding line into an array .
Now, we have the co-ordinates of image from which we need to separate the
image.
10. Algo III continues….
Again, we have created an array of BufferedImage type to store the separated images.
BufferedImage imgs[ ] = new BufferedImage[size];
Then we defined the dimension for the portion of image to that array, which is need to
be separated.
We used predefined method drawImage() for separating the image.
Input : Output :
11. Algo IV (Part 1 : Font Text)
Now, we split each letter (font text) from the word.
We start searching vertically, all the portion of text (in black) area and
count it separately for every vertical line and store it into an array.
Then we look for that line which has white portion and the previous line
should have some text portion and we shift the value to adjust the gap
then store the co-ordinate of that line into an array.
Now, we have the co-ordinates of image from which we need to separate
the image.
12. Algo IV (Part 1 : Font Text) continues….
Again, we have created an array of BufferedImage type to store the separated images.
BufferedImage imgs[ ] = new BufferedImage[size];
Then we defined the dimension for the portion of image to that array, which is need to
be separated.
We used predefined method drawImage() for separating the image.
Input : Output :
13. Algo IV (Part 2 : Hand written Text)
Now, we split each letter (hand written text) from the word.
We start searching vertically, all the portion of text (in black) area and
count it separately for every vertical line and store it into an array.
Then we look for that line which has minimum portion of text and store
the co-ordinate of that line into an array.
We find the line which is next to the stored co-ordinate of minimum
portion of text and if it is more than all the minimum portions stored in the
array then we shift the value to adjust the gap then store the co-ordinate
of that line into another array.
Now, we have the co-ordinates of image from which we need to separate
the image.
14. Algo IV (Part 2 : Hand written Text)
continues….
Again, we have created an array of BufferedImage type to store the separated images.
BufferedImage imgs[ ] = new BufferedImage[size];
Then we defined the dimension for the portion of image to that array, which is need to
be separated.
We used predefined method drawImage() for separating the image.
Input : Output :
15. Algo V (Part 1) :
We convert the obtained image of letter into 100x100 pixels.
For this purpose we convert the size of image into 100x100 pixels.
We used predefined method drawImage() for changing the pixels of the
image.
Input : Output :
16. Algo V (Part 2) :
We have defined some strips condition for letters (particularly for A, B, C &
D).
We match the image with predefined strips of co-ordinate.
If the image matches every strips condition then it get validated for that
letter.
And, we display the corresponding letter as an output.
Input : Output :
ABCD
17. Advantage :
Image to text converter utility helps in format portability and compatibility
that serves the purpose of using conversion from one format to another. In
the present scenario, interchangeable formats are more in demand and
software developers around the world need utilities that can convert files
from one format to another easily and without too much hassle. This is
where the ‘Image To Text Converter’ utility comes into play and the
benefits of using the same are required. Further, many of the media
houses use the converted files to store and retrieve data whenever they
need. This helps in files restoring of image files at one's convenience
making life easier for everyone in the process.
18. Limitations :
The first co-ordinate (0,0) of the image should not be the portion of text.
The handwritten text extracting process is successful for few letters yet.
The joining portion of the hand written text should not have more
thickness.
19. Conclusion :
By this project we can come to the conclusion that we can convert image’s texts into
editable text.