This document discusses text detection and character recognition from images. It begins with an introduction and then discusses the aims, objectives, motivation and problem statement. It reviews relevant literature on segmentation and recognition techniques. The document then describes the methodology used, including preprocessing, segmentation using vertical projections and connected components, and recognition using pixel counting, projections, template matching, Fourier descriptors and heuristic filters. It presents results from four experiments comparing different segmentation and recognition methods. The discussion analyzes results and limitations. The conclusion finds that segmentation works best with connected components while recognition works best with template matching, Fourier descriptors and heuristic filters.
With so much of our lives computerized, it is vitally important that machines and humans can understand one another and pass information back and forth. Mostly computers have things their way we have to & talk to them through relatively crude devices such as keyboards and mice so they can figure out what we want them to do. However, when it comes to processing more human kinds of information, like an old-fashioned printed book or a letter scribbled with a fountain pen, computers have to work much harder. That is where optical character recognition (OCR) comes in. Here we process the image, where we apply various pre-processing techniques like desk wing, binarization etc. and algorithms like Tesseract to recognize the characters and give us the final document. T.Gnana Prakash | K. Anusha"Text Extraction from Image using Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2501.pdf http://www.ijtsrd.com/computer-science/simulation/2501/text-extraction-from-image-using-python/tgnana-prakash
Text detection and recognition from natural sceneshemanthmcqueen
Text characters in natural scenes and surroundings provide us with valuable information about the place and even provide us with some legal/important information. Hence it’s very important for us to detect such text and recognise them which helps a lot. But , it’s not really easy to recognize those text information because of the diverse backgrounds and fonts used for the text. In this paper, a method is proposed to extract the text information from the surroundings. First, a character descriptor is designed with existing standard detectors and descriptors. Then, character structure is modeled at each character class by designing stroke configuration maps.In natural scenes , the text part is generally found on nearby sign boards and other objects. The extraction of such text is difficult because of noisy backgrounds and diverse fonts and text sizes. But many applications have been proven to be efficient in extraction of text from surroundings. For this , the method of text extraction is divided into two processes;
Text detection
Text recognition
Handwritten Character Recognition: A Comprehensive Review on Geometrical Anal...iosrjce
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.
With so much of our lives computerized, it is vitally important that machines and humans can understand one another and pass information back and forth. Mostly computers have things their way we have to & talk to them through relatively crude devices such as keyboards and mice so they can figure out what we want them to do. However, when it comes to processing more human kinds of information, like an old-fashioned printed book or a letter scribbled with a fountain pen, computers have to work much harder. That is where optical character recognition (OCR) comes in. Here we process the image, where we apply various pre-processing techniques like desk wing, binarization etc. and algorithms like Tesseract to recognize the characters and give us the final document. T.Gnana Prakash | K. Anusha"Text Extraction from Image using Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2501.pdf http://www.ijtsrd.com/computer-science/simulation/2501/text-extraction-from-image-using-python/tgnana-prakash
Text detection and recognition from natural sceneshemanthmcqueen
Text characters in natural scenes and surroundings provide us with valuable information about the place and even provide us with some legal/important information. Hence it’s very important for us to detect such text and recognise them which helps a lot. But , it’s not really easy to recognize those text information because of the diverse backgrounds and fonts used for the text. In this paper, a method is proposed to extract the text information from the surroundings. First, a character descriptor is designed with existing standard detectors and descriptors. Then, character structure is modeled at each character class by designing stroke configuration maps.In natural scenes , the text part is generally found on nearby sign boards and other objects. The extraction of such text is difficult because of noisy backgrounds and diverse fonts and text sizes. But many applications have been proven to be efficient in extraction of text from surroundings. For this , the method of text extraction is divided into two processes;
Text detection
Text recognition
Handwritten Character Recognition: A Comprehensive Review on Geometrical Anal...iosrjce
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.
This slides about brief Introduction to Image Restoration Techniques. How to estimate the degradation function, noise models and its probability density functions.
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.
We will use 7 emotions namely - We have used 7 emotions namely - 'Angry', 'Disgust'濫, 'Fear', 'Happy', 'Neutral', 'Sad'☹️, 'Surprise' to train and test our algorithm using Convolution Neural Networks.
This slides about brief Introduction to Image Restoration Techniques. How to estimate the degradation function, noise models and its probability density functions.
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.
We will use 7 emotions namely - We have used 7 emotions namely - 'Angry', 'Disgust'濫, 'Fear', 'Happy', 'Neutral', 'Sad'☹️, 'Surprise' to train and test our algorithm using Convolution Neural Networks.
If your organization is considering or has deployed optical character recognition software for your in-house utility bill data processing needs, you may be doing more work than you need to.
Want to learn how to streamline your utility bill and interval data collection processes? Urjanet's Director of Product Management D.j. Amis describes the variety of ways in which utility bill and interval data can be accessed and gathered. He also shares why it might make sense for your organization to partner with a service provider that can automate the entire process for you, so you can focus on what really matters to your business.
Did you miss out on this year's SPARK event? Don't worry! Our team of dedicated utility data experts worked to collect the top insights and key takeaways from this year's event. Check out this SlideShare to learn more about the industry insights and trends that were hot topics as this year's event.
Miss out on our latest webinar? Don't worry, we've put together a brief webinar recap to find out more about what Urjanet's Erik Becker, VP Sales, and eCredable’s CEO, Steve Ely, think of today’s credit scoring models, the shortcomings and limitations, and how new proprietary models could be the answer to giving the millions of underbanked and unbanked American consumers a more sufficient method of credit scoring.
In this webinar recap you’ll also gain insight into the sentiments around this new proprietary scoring model as the team reviews the results of a recent survey conducted by Urjanet of nearly 900 American consumers. Check out the webinar recap to learn more!
Faster Training Algorithms in Neural Network Based Approach For Handwritten T...CSCJournals
Handwritten text and character recognition is a challenging task compared to recognition of handwritten numeral and computer printed text due to its large variety in nature. As practical pattern recognition problems uses bulk data and there is a one step self sufficient deterministic theory to resolve recognition problems by calculating inverse of Hessian Matrix and multiplication the inverse matrix it with first order local gradient vector. But in practical cases when neural network is large the inversing operation of the Hessian Matrix is not manageable and another condition must be satisfied the Hessian Matrix must be positive definite which may not be satishfied. In these cases some repetitive recursive models are taken. In several research work in past decade it was experienced that Neural Network based approach provides most reliable performance in handwritten character and text recognition but recognition performance depends upon some important factors like no of training samples, reliable features and no of features per character, training time, variety of handwriting etc. Important features from different types of handwriting are collected and are fed to the neural network for training. It is true that more no of features increases test efficiency but it takes longer time to converge the error curve. To reduce this training time effectively proper train algorithm should be chosen so that the system provides best train and test efficiency in least possible time that is to provide the system fastest intelligence. We have used several second order conjugate gradient algorithms for training of neural network. We have found that Scaled Conjugate Gradient Algorithm , a second order training algorithm as the fastest for training of neural network for our application. Training using SCG takes minimum time with excellent test efficiency. A scanned handwritten text is taken as input and character level segmentation is done. Some important and reliable features from each character are extracted and used as input to a neural network for training. When the error level reaches into a satisfactory level (10 -12 ) weights are accepted for testing a test script. Finally a lexicon matching algorithm solves the minor misclassification problems.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
Template matching is a basic method in image analysis to extract useful information from images. In this
paper, we suggest a new method for pattern matching. Our method transform the template image from two
dimensional image into one dimensional vector. Also all sub-windows (same size of template) in the
reference image will transform into one dimensional vectors. The three similarity measures SAD, SSD, and
Euclidean are used to compute the likeness between template and all sub-windows in the reference image
to find the best match. The experimental results show the superior performance of the proposed method
over the conventional methods on various template of different sizes.
A COMPARATIVE ANALYSIS OF RETRIEVAL TECHNIQUES IN CONTENT BASED IMAGE RETRIEVALcscpconf
Basic group of visual techniques such as color, shape, texture are used in Content Based Image Retrievals (CBIR) to retrieve query image or sub region of image to find similar images in image database. To improve query result, relevance feedback is used many times in CBIR to help user to express their preference and improve query results. In this paper, a new approach for image retrieval is proposed which is based on the features such as Color Histogram, Eigen Values and Match Point. Images from various types of database are first identified by using edge detection techniques .Once the image is identified, then the image is searched in the particular database, then all related images are displayed. This will save the retrieval time. Further to retrieve the precise query image, any of the three techniques are used and comparison is done w.r.t. average retrieval time. Eigen value technique found to be the best as compared with other two techniques.
A comparative analysis of retrieval techniques in content based image retrievalcsandit
Basic group of visual techniques such as color, shape, texture are used in Content Based Image
Retrievals (CBIR) to retrieve query image or sub region of image to find similar images in
image database. To improve query result, relevance feedback is used many times in CBIR to
help user to express their preference and improve query results. In this paper, a new approach
for image retrieval is proposed which is based on the features such as Color Histogram, Eigen
Values and Match Point. Images from various types of database are first identified by using
edge detection techniques .Once the image is identified, then the image is searched in the
particular database, then all related images are displayed. This will save the retrieval time.
Further to retrieve the precise query image, any of the three techniques are used and
comparison is done w.r.t. average retrieval time. Eigen value technique found to be the best as
compared with other two techniques.
PERFORMANCE EVALUATION OF FUZZY LOGIC AND BACK PROPAGATION NEURAL NETWORK FOR...ijesajournal
ABSTRACT
Fuzzy c-mean is one of the efficient tools used in character recognition. Back propagation neural network is another powerful that may be used in such field. A comparison between fuzzy c-mean and BP neural network classifiers are presented in this research to obtain the performance of both classifiers. The comparison was based on recognition efficiency; this efficiency was evaluated as the ratio of the number of assigned characters with unknown one to the number of character set related to that character. The fuzzy C-mean and BP neural network algorithms were tested on a set of hand written and machine printed dataset named Chars74K dataset using Matlab (2016 b) programming language and the result was that neural network classifier gave 82% recognition efficiency while fuzzy c –mean gave 78%. Neural network classifier is more superior than fuzzy C-mean in recognition due to the limitations of processing time of fuzzy C-mean that requires smaller image size and eventually this will cause less efficiency.
TEMPLATE MATCHING TECHNIQUE FOR SEARCHING WORDS IN DOCUMENT IMAGESIJCI JOURNAL
Template matching technique is useful for searching and finding the location of a template image (Small part of image) in the larger image. This technique is also used in Optical Character Recognition (OCR) tools and these tools are used for converting the scanned document images into normal text. Template matching technique is used to find and recognize the template image which is found in the given input image. In this research work, template matching technique is applied for scanned document images which contains characters (both uppercase and lowercase) and numerals. In order to perform the comparison of the template image with the input image we have used Performance Index method and it is compared with the normalized cross correlation and cross correlation methods. Different types of comparisons done in this work are, (i) comparing single character from a word, sentence and paragraph; (ii) comparing multiple characters (words) from a word, sentence and paragraph.
License plate recognition system is one of the core technologies in intelligent traffic control. In this paper, a new and tunable algorithm which can detect multiple license plates in high resolution applications is proposed. The algorithm aims at investigation into and identification of the novel Iranian and some European countries plate, characterized by both inclusion of blue area on it and its geometric shape. Obviously, the suggested algorithm contains suitable velocity due to not making use of heavy pre-processing operation such as image-improving filters, edge-detection operation and omission of noise at the beginning stages. So, the recommended method of ours is compatible with model-adaptation, i.e., the very blue section of the plate so that the present method indicated the fact that if several plates are included in the image, the method can successfully manage to detect it. We evaluated our method on the two Persian single vehicle license plate data set that we obtained 99.33, 99% correct recognition rate respectively. Further we tested our algorithm on the Persian multiple vehicle license plate data set and we achieved 98% accuracy rate. Also we obtained approximately 99% accuracy in character recognition stage.
What is pattern recognition (lecture 4 of 6)Randa Elanwar
In this series I intend to simplify a beautiful branch of computer science that we as humans use it in everyday life without knowing. Pattern recognition is a sub-branch of the computer vision research and is tightly related to digital signal processing research as well as machine learning and artificial intelligence.
Introducing New Parameters to Compare the Accuracy and Reliability of Mean-Sh...sipij
Mean shift algorithms are among the most functional tracking methods which are accurate and have almost simple computation. Different versions of this algorithm are developed which are differ in template updating and their window sizes. To measure the reliability and accuracy of these methods one should normally rely on visual results or number of iteration. In this paper we introduce two new parameters which can be used to compare the algorithms especially when their results are close to each other.
This paper proposes a multiclass recognition scheme which uses multiple feature trees with an
extended scoring method evolved from TF-IDF. Feature trees consisting of different feature
descriptors such as SIFT and SURF are built by the hierarchical k-means algorithm. The
experimental results show that the proposed scoring method combing with the proposed
multiple feature trees yields high accuracy for multiclass recognition and achieves significant
improvement compared to methods using a single feature tree with original TF-IDF.
Multiclass Recognition with Multiple Feature Treescsandit
This paper proposes a multiclass recognition scheme which uses multiple feature trees with an
extended scoring method evolved from TF-IDF. Feature trees consisting of different feature
descriptors such as SIFT and SURF are built by the hierarchical k-means algorithm. The
experimental results show that the proposed scoring method combing with the proposed
multiple feature trees yields high accuracy for multiclass recognition and achieves significant
improvement compared to methods using a single feature tree with original TF-IDF.
A smart environment is one that is able to identify people, interpret their actions, and react appropriately. Thus, one of the most important building blocks of smart environments is a person identification system. Face recognition devices are ideal for such systems, since they have recently become fast, cheap, unobtrusive, and, when combined with voice-recognition, are very robust against changes in the environment.
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.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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.
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.
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.
Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...
Text Detection and Recognition
1. Text Detection and Character Recognition from Images BadruzNasrin Bin Basri 1051101534 Supervisor : MohdHaris Lye Abdullah 1
2. Contents Introduction 1 Literature review 2 Method Used 3 Experiment and Result 4 Future works 5 2
3. Aims and objectives Student 2 Recognition - Recognize each of the character in the detected text region using a suitable algorithm Segmentation - Separate the text region into its individual characters. 3 The aim of this project is to detect, extract and recognize text from images, particularly license car plate.
4. Motivation 4 Text detection and recognition in general have quite a lot of relevant application for automatic indexing or information retrieval such document indexing, content-based image retrieval, and license car plate recognition which further opens up the possibility for more improved and advanced systems.
5. Problem statement 5 To segment the image to individual characters, we need to find the characteristic to be used as boundary to segment the image. To classify, we need to use the best template to compare with the segmented image and to determine how the template will be used to compare with the image.
8. Character size in PayPal HIP is fixedStudy on Malaysian License Plate Recognition by Othman [3] proposed a model specifically to detect and recognize the text in Malaysian license plate. For segmentation, connected components method has been proposed however this method only can use if the license plates are single row license plate and the study only been made on single row license plate. Same method also been use by Ganapathy and Lui in [4]
9. 7 Literature review Recognition Klouverin research on recognition text in PayPal HIP [1] and Ho C. H. et. al in research on License Plate Recognition(LPR) [2] used Templates Matching to recognize the characters in image. Klouver detailed the matching classifier into four types of classifier that are Pixel Counting, Horizontal Projection, Vertical Projection and Template Correlations. Klouver’s experiment proved that the best classifier is Vertical projection and Template Correlation where both of this classifier yield 100% accuracy. Fixed type of font in image(PayPal HIP) makes it very easy to distinguish between different characters using templates matching. There are no other research that yield 100% accuracy.
10. 8 Literature review Recognition Study on Malaysian License Plate Recognition by Othman [3],Ganapathy and Lui in [4], M.Fukumi et. Al [6] , AnasJ.A. Husain et. Al [7] used Neural Network to recognized the text. Compared to templates matching, neural network consume more time. Neural network also need training before it can be used and it only can achieve high accuracy if the sampled image is almost same with the training images. Andrew Vogt and Joe G. Bared [5] concluded the disadvantages of neural network are : Minimizing overfitting in neural networks requires a great deal of computational effort The individual relations between the input variables and the output variables are not developed by engineering judgment so the model tends to be a black box or input/output table without analytical basis and to make the accuracy level high the sample size has to be large.
11. 9 Literature review Recognition Jared Hopkins and Tim Anderson in [9], used Fourier Descriptor to recognize text in image. In most of the researches, Fourier descriptor been used to recognize more complex shape such as for logo classification by Folkers and Samet [10] and for Sinhala Script by Rohana, Ruvan and Kevin [11]. Basically there are no research on LPR using Fourier descriptor(FD), hence, this research will also test the usage of FD to recognize text in Malaysian License Plate. WisamAl Faqheri and SyamsiahMashohor in A Real-Time Malaysian Automatic License Plate Recognition (M-ALPR) using Hybrid Fuzzy [12] used the hybrid Fuzzy method to recognize the license number. Compared to other study previously done on license plate detection where almost all of the previous work relied on a single method like template matching or neural network Wisamand Syamsiah proposed combination of more than one method based on the type of license plate.
13. Make template To create templete.mat to be use for classification: 11 … … Matrix size 24 X 42 X 36 Saved as template.mat 36 images of characters Size = 42 X 24
16. Segmentation – Vertical Projections Weaknesses 14 Image that failed to be segmented by vertical projection
17. The segmentation character involves the following steps: Scan the image from left to right to find ‘on’ pixel. If on pixel been found, all ‘on’ pixel connected to the detected on pixel will be extracted segmented as a pixel. The process will be repeated until it reach end right of the image. 15 Segmentation – Connected Components
19. Corr2 𝑟=mn𝑖𝑚𝑛−𝑖(𝑗𝑚𝑛−𝑗)(mn(𝑖mn− 𝑖)2)(mn(𝑗mn− 𝑗)2) Where 𝑖 is the mean of the input matrix i and 𝑗 is the mean of the input matrix j. 0 < r < 1 1 mean i and j is exactly same while 0 mean the i and j not same at all. 17
23. Recognition – Fourier Descriptor Following is the detailed steps on extracting and comparing the Fourier Descriptor (FD) Edging 21 U= 𝑥0𝑦0𝑥1𝑦1...𝑥𝑛𝑦𝑛
24. Recognition – Fourier Descriptor Extracting FD – 1 D Discrete Fourier Transform (DFT) been done to the complex vector U to get the frequency domain of the boundaries using the following equation: 22 F=𝐹𝐹𝑇𝑈=𝑘=0𝑁−1Uk− 2πN𝑘𝜇
26. Recognition – Fourier Descriptor Images of ‘E’ reconstructed from (a) n = 4 (b) n = 8 (c) n = 10 (d) n = 15 (e) n = 25 (f) n = 30 (g) n = 278 Resize FD – As FD contains information of all information of the ‘on’ pixel, the size of FD is number of on pixel. To make it comparable with other FD it need to be resized to predefined number of descriptor, Figure 3.9 show different shape reconstructed using different number of descriptor. As to resize the FD to n descriptor, function shiftfft in Matlab will remove low frequency descriptor leaving only n-th highest descriptor. 24
27. Recognition – Fourier Descriptor Compare FD – CompareFD a measure of the difference between two inputs FD. It will quantify the difference between FDs. Higher values of different mean the two FDs are far apart in shape. The extracted FD, I can be compared with using the following algorithm: CompareFD(I,T) D ← ø for each Templates 𝑡𝑖∈T do diff = -1 if length(𝑡𝑖) = length (I) do diff ← sum(𝑎𝑏𝑠(𝑡𝑖−𝐼)2) return k such that 𝑟𝑘=min(𝑅) 25
28.
29.
30. Recognition – Heuristic Filter 28 Notice that for ‘H’, x line passed through 2 white line and y passed through 2 white line, for’ W’, x passed 3 white line and y passed 2 white line while ‘M’ is opposite.
31. Experiment 1: Comparison between Different Segmentation Method and Different Templates Matching Classifier 29 Template with size 42 X 24 was created using images of 36 characters. To conduct the experiment, all 125 images have been renamed as their ground truth and saved in a folder. A Matlab script as included in appendices was created to load all the images in the folder as well as their name and then perform preprocess segmentation and recognition to all of the images. Then the result of the segmentation and recognition as well as time needed to recognize a number plate were recorded and calculated using following equation. The result will also be analyzed automatically by the Matlab script. Some of images that been used in the experiment
32. Segmentation Accuracy was calculated using formula : 𝐴𝑆𝑒𝑔𝑚𝑒𝑛𝑡=𝑁𝑆𝑒𝑔𝑚𝑒𝑛𝑡𝑒𝑑𝑁𝑆𝑎𝑚𝑝𝑙𝑒𝐶h𝑎𝑟 x 100% Where 𝐴𝑆𝑒𝑔𝑚𝑒𝑛𝑡 is segmentation accuracy, 𝑁𝑆𝑒𝑔𝑚𝑒𝑛𝑡𝑒𝑑 is number of correctly segmented character and 𝑁𝑆𝑎𝑚𝑝𝑙𝑒𝐶h𝑎𝑟 is number of characters in sample. Classification Accuracy was calculated using formula : 𝐴𝐶h𝑎𝑟=𝑁𝑅𝑒𝑐𝑜𝑔𝑛𝑖𝑧𝑒𝑁𝑆𝑒𝑔𝑚𝑒𝑛𝑡𝑒𝑑x 100% Where 𝐴𝐶h𝑎𝑟 is recognition accuracy, 𝑁𝑅𝑒𝑐𝑜𝑔𝑛𝑖𝑧𝑒 is number of correctly recognized character and 𝑁𝑆𝑒𝑔𝑚𝑒𝑛𝑡𝑒𝑑 is number of characters that had been correctly segmented. Classification Accuracy was calculated using formula : 𝑡𝐶h𝑎𝑟=𝑇𝑁𝑆𝑎𝑚𝑝𝑙𝑒 Where 𝑡𝐶h𝑎𝑟 is average recognition time, 𝑇 total running time to recognize all sample images and 𝑁𝑆𝑎𝑚𝑝𝑙𝑒 is number of sample images. 30 Experiment 1: Comparison between Different Segmentation Method and Different Templates Matching Classifier
33. Then the experiment repeated four times using connected components as segmentation method and the following as recognition classifier: Pixel Count Vertical Projection Horizontal Projection Templates Correlation 31 Experiment 1: Comparison between Different Segmentation Method and Different Templates Matching Classifier
34. Result 32 Comparison on segmentation by Vertical Projection and Connected Components Comparison on Template Matching using different classifier
35. Experiment repeated two times using connected components as segmentation method and the following as recognition classifier: Templates Correlation Fourier Descriptor 33 Experiment 2: Comparison between Template Correlation and Fourier Descriptor
36. Result 34 Comparison on Recognition by Templates Correlation and Fourier Descriptor
37. Experiment repeated with introducing context in the algorithm 35 Experiment 3: Improvement on LPR Using Context Approach Result Comparison on recognition by Templates Correlation after context been introduced
38. Experiment repeated with introducing hybrid in the algorithm 36 Experiment 4: Improvement on LPR Using Hybrid method Result Comparison on recognition by Templates Correlation after hybrid been introduced
39. Discussion 37 Image that failed to be segmented using connected components Preprocessed image after erosion
40. Why heuristic filters failed ? 38 Image that failed to be recognized due to change in Euler number
41. Why Fourier Descriptors failed ? 39 Image that failed to be recognized due to unsmoothed image Image that failed to be recognized due to rotation invariant
42. Conclusion The objective of this paper is to segment and recognize characters in image have been achieved. Even the segmentation accuracy from the experiment is 100% the result during real application may be lower due to limited set of picture used in experiment. However, this shown that segmentation using connected components is best method to segmenting the image. After several experiments been done to find the best method to recognize the characters with highest accuracy and considerable amount of time, the best way is by using templates correlations as main recognition method with Fourier Descriptor and several heuristic approach as filters. Experiments have found that this method’s recognition accuracy is 98.46%. 40
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