The document presents a method for detecting text lines in camera-captured images using MATLAB GUI. The method uses Maximally Stable Extremal Regions (MSER) algorithm to detect character regions in the images. Unwanted regions are removed based on geometric properties. Optical Character Recognition (OCR) is then used to recognize characters and detect word regions. The method is tested on scene text, street view, and handwritten text datasets, achieving over 50% accuracy for scene images, 74% for street view images, and 68% for handwritten images. Results are presented comparing performance on images taken with mobile and digital cameras under various conditions like indoor, outdoor, shadow, and book cover.
IRJET- Image to Text Conversion using TesseractIRJET Journal
This document discusses using Tesseract OCR engine to convert images containing text into editable text files. It begins with an abstract describing how digital images often contain text data that users need to access and edit digitally. Tesseract is an open-source OCR tool that uses neural networks like LSTM to recognize text in images with high accuracy and convert it into editable text. It then reviews existing OCR methods before describing Tesseract's image processing and recognition steps in more detail. The document also notes that the converted text could then be used to create audio files for visually impaired users to hear the text content.
CONTENT RECOVERY AND IMAGE RETRIVAL IN IMAGE DATABASE CONTENT RETRIVING IN TE...Editor IJMTER
Digital Images are used in magazines, blogs, website, television and more. Digital image processing
techniques are used for feature selection, pattern extraction classification and retrieval requirements. Color, texture
and shape features are used in the image processing. Digital images processing also supports computer graphics
and computer vision domains. Scene text recognition is performed with two schemes. They are character
recognizer and binary character classifier models. A character recognizer is trained to predict the category of a
character in an image patch. A binary character classifier is trained for each character class to predict the existence
of this category in an image patch. Scene text recognition is performed on detected text regions. Pixel-based layout
analysis method is adopted to extract text regions and segment text characters in images. Text character
segmentation is carried out with color uniformity and horizontal alignment of text characters. Discriminative
character descriptor is designed by combining several feature detectors and descriptors. Histogram of Oriented
Gradients (HOG) is used to identify the character descriptors. Character structure is modeled at each character
class by designing stroke configuration maps. The scene text extraction scheme is also supports for smart mobile
devices. Text recognition methods are used with text understanding and text retrieval applications. The text
recognition scheme is enhanced with content based image retrieval process. The system is integrated with
additional representative and discriminative features for text structure modeling process. The system is enhanced to
perform text and word level recognition using lexicon analysis. The training process is included with word
database update task.
IRJET- Image based Information RetrievalIRJET Journal
This document discusses content-based image retrieval (CBIR) for retrieving images based on visual similarity. It focuses on using CBIR to match images of monuments for tourism applications. The paper describes extracting shape features using edge histogram descriptors to divide images into sub-images and compare edge distributions. An experiment matches images of Humayun's Tomb and the Statue of Liberty by comparing their edge magnitude values across sub-images. Similar edge distributions between two images' sub-images indicates similarity in shape and matches the images. The paper concludes CBIR using shape features can effectively match similar images of monuments to provide relevant information to users.
This document compares the results of retrieving songket motifs from a digital repository using a sketching technique versus a keyword technique. It finds that sketch-based retrieval was more successful, with 80% of sketch queries returning more relevant results than keyword queries. The document provides background on songket as Malaysian cultural heritage and discusses prior research on sketch-based image retrieval techniques. It evaluates existing songket websites and then describes the methodology, design, and development of a songket motif retrieval prototype using sketching.
IRJET- Detection and Recognition of Text for Dusty Image using Long Short...IRJET Journal
The document describes a proposed system for detecting and recognizing text in dusty images. The system has two main modules: 1) preprocessing and enhancement of the dusty input image, and 2) detection and recognition of text regions. The preprocessing and enhancement uses median filtering, color space conversion to Lab color space, and contrast limited adaptive histogram equalization (CLAHE) to improve the appearance and visibility of text in the dusty image. The second module then detects text regions using maximally stable extremal regions (MSER) and recognizes characters using a long short-term memory (LSTM) neural network, which is well-suited for this challenging task.
Face Recognition for Human Identification using BRISK Feature and Normal Dist...ijtsrd
Face recognition is a kind of automatic human identification from face images has been performed widely research in image processing and machine learning. Face image, facial information of the person is presented and unique information for each person even two person possessed the same face. We propose a methodology for automatic human classification based on Binary Robust Invariant Scalable Keypoints BRISK feature of face images and the normal distribution model. In our proposed methodology, the normal distribution model is used to represent the statistical information of face image as a global feature. The human name is the output of the system according to the input face image. Our proposed feature is applied with Artificial Neural Networks to recognize face for human identification. The proposed feature is extracted from the face image of the Extended Yale Face Database B to perform human identification and highlight the properties of the proposed feature. Khin Mar Thi "Face Recognition for Human Identification using BRISK Feature and Normal Distribution Model" 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/ijtsrd26589.pdfPaper URL: https://www.ijtsrd.com/computer-science/multimedia/26589/face-recognition-for-human-identification-using-brisk-feature-and-normal-distribution-model/khin-mar-thi
A Texture Based Methodology for Text Region Extraction from Low Resolution Na...CSCJournals
Automated systems for understanding display boards are finding many applications useful in guiding tourists, assisting visually challenged and also in providing location aware information. Such systems require an automated method to detect and extract text prior to further image analysis. In this paper, a methodology to detect and extract text regions from low resolution natural scene images is presented. The proposed work is texture based and uses DCT based high pass filter to remove constant background. The texture features are then obtained on every 50x50 block of the processed image and potential text blocks are identified using newly defined discriminant functions. Further, the detected text blocks are merged and refined to extract text regions. The proposed method is robust and achieves a detection rate of 96.6% on a variety of 100 low resolution natural scene images each of size 240x320.
This document describes a content-based image retrieval system that uses fractal signature analysis and foreground feature extraction. It begins with an introduction to content-based image retrieval and discusses challenges with metadata-based systems. It then proposes a system that uses fractal scanning to generate signatures for the RGB color components of extracted foreground objects. Features are extracted from these signatures using discrete cosine transform and Fourier descriptors to allow retrieval of images even with distortions. The document concludes by discussing constraints of the current system and potential future enhancements.
IRJET- Image to Text Conversion using TesseractIRJET Journal
This document discusses using Tesseract OCR engine to convert images containing text into editable text files. It begins with an abstract describing how digital images often contain text data that users need to access and edit digitally. Tesseract is an open-source OCR tool that uses neural networks like LSTM to recognize text in images with high accuracy and convert it into editable text. It then reviews existing OCR methods before describing Tesseract's image processing and recognition steps in more detail. The document also notes that the converted text could then be used to create audio files for visually impaired users to hear the text content.
CONTENT RECOVERY AND IMAGE RETRIVAL IN IMAGE DATABASE CONTENT RETRIVING IN TE...Editor IJMTER
Digital Images are used in magazines, blogs, website, television and more. Digital image processing
techniques are used for feature selection, pattern extraction classification and retrieval requirements. Color, texture
and shape features are used in the image processing. Digital images processing also supports computer graphics
and computer vision domains. Scene text recognition is performed with two schemes. They are character
recognizer and binary character classifier models. A character recognizer is trained to predict the category of a
character in an image patch. A binary character classifier is trained for each character class to predict the existence
of this category in an image patch. Scene text recognition is performed on detected text regions. Pixel-based layout
analysis method is adopted to extract text regions and segment text characters in images. Text character
segmentation is carried out with color uniformity and horizontal alignment of text characters. Discriminative
character descriptor is designed by combining several feature detectors and descriptors. Histogram of Oriented
Gradients (HOG) is used to identify the character descriptors. Character structure is modeled at each character
class by designing stroke configuration maps. The scene text extraction scheme is also supports for smart mobile
devices. Text recognition methods are used with text understanding and text retrieval applications. The text
recognition scheme is enhanced with content based image retrieval process. The system is integrated with
additional representative and discriminative features for text structure modeling process. The system is enhanced to
perform text and word level recognition using lexicon analysis. The training process is included with word
database update task.
IRJET- Image based Information RetrievalIRJET Journal
This document discusses content-based image retrieval (CBIR) for retrieving images based on visual similarity. It focuses on using CBIR to match images of monuments for tourism applications. The paper describes extracting shape features using edge histogram descriptors to divide images into sub-images and compare edge distributions. An experiment matches images of Humayun's Tomb and the Statue of Liberty by comparing their edge magnitude values across sub-images. Similar edge distributions between two images' sub-images indicates similarity in shape and matches the images. The paper concludes CBIR using shape features can effectively match similar images of monuments to provide relevant information to users.
This document compares the results of retrieving songket motifs from a digital repository using a sketching technique versus a keyword technique. It finds that sketch-based retrieval was more successful, with 80% of sketch queries returning more relevant results than keyword queries. The document provides background on songket as Malaysian cultural heritage and discusses prior research on sketch-based image retrieval techniques. It evaluates existing songket websites and then describes the methodology, design, and development of a songket motif retrieval prototype using sketching.
IRJET- Detection and Recognition of Text for Dusty Image using Long Short...IRJET Journal
The document describes a proposed system for detecting and recognizing text in dusty images. The system has two main modules: 1) preprocessing and enhancement of the dusty input image, and 2) detection and recognition of text regions. The preprocessing and enhancement uses median filtering, color space conversion to Lab color space, and contrast limited adaptive histogram equalization (CLAHE) to improve the appearance and visibility of text in the dusty image. The second module then detects text regions using maximally stable extremal regions (MSER) and recognizes characters using a long short-term memory (LSTM) neural network, which is well-suited for this challenging task.
Face Recognition for Human Identification using BRISK Feature and Normal Dist...ijtsrd
Face recognition is a kind of automatic human identification from face images has been performed widely research in image processing and machine learning. Face image, facial information of the person is presented and unique information for each person even two person possessed the same face. We propose a methodology for automatic human classification based on Binary Robust Invariant Scalable Keypoints BRISK feature of face images and the normal distribution model. In our proposed methodology, the normal distribution model is used to represent the statistical information of face image as a global feature. The human name is the output of the system according to the input face image. Our proposed feature is applied with Artificial Neural Networks to recognize face for human identification. The proposed feature is extracted from the face image of the Extended Yale Face Database B to perform human identification and highlight the properties of the proposed feature. Khin Mar Thi "Face Recognition for Human Identification using BRISK Feature and Normal Distribution Model" 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/ijtsrd26589.pdfPaper URL: https://www.ijtsrd.com/computer-science/multimedia/26589/face-recognition-for-human-identification-using-brisk-feature-and-normal-distribution-model/khin-mar-thi
A Texture Based Methodology for Text Region Extraction from Low Resolution Na...CSCJournals
Automated systems for understanding display boards are finding many applications useful in guiding tourists, assisting visually challenged and also in providing location aware information. Such systems require an automated method to detect and extract text prior to further image analysis. In this paper, a methodology to detect and extract text regions from low resolution natural scene images is presented. The proposed work is texture based and uses DCT based high pass filter to remove constant background. The texture features are then obtained on every 50x50 block of the processed image and potential text blocks are identified using newly defined discriminant functions. Further, the detected text blocks are merged and refined to extract text regions. The proposed method is robust and achieves a detection rate of 96.6% on a variety of 100 low resolution natural scene images each of size 240x320.
This document describes a content-based image retrieval system that uses fractal signature analysis and foreground feature extraction. It begins with an introduction to content-based image retrieval and discusses challenges with metadata-based systems. It then proposes a system that uses fractal scanning to generate signatures for the RGB color components of extracted foreground objects. Features are extracted from these signatures using discrete cosine transform and Fourier descriptors to allow retrieval of images even with distortions. The document concludes by discussing constraints of the current system and potential future enhancements.
Fingerprints are imprints formed by friction
ridges of the skin and thumbs. They have long been used for
identification because of their immutability and individuality.
Immutability refers to the permanent and unchanging character
of the pattern on each finger. Individuality refers to the
uniqueness of ridge details across individuals; the probability
that two fingerprints are alike is about 1 in 1.9x1015. In despite of
this improvement which is adopted by the Federal Bureau of
Investigation (FBI), the fact still is “The larger the fingerprint
files became, the harder it was to identify somebody from their
fingerprints alone. Moreover, the fingerprint requires one of the
largest data templates in the biometric field”. The finger data
template can range anywhere from several hundred bytes to over
1,000 bytes depending upon the level of security that is required
and the method that is used to scan one's fingerprint. For these
reasons this work is motivated to present another way to tackle
the problem that is relies on the properties of Vector
Quantization coding algorithm.
This document summarizes a research paper on tracking multiple targets using the mean shift algorithm. It begins by stating that multi-target tracking is challenging due to factors like noise, clutter, occlusions, and sudden changes in velocity. The mean shift algorithm is then introduced as a kernel-based tracking method that works by iteratively shifting target locations to their mean shifts. Targets are represented using histograms within elliptical regions. The Bhattacharyya coefficient is used to measure similarity between target models and candidates. Experimental results on a video sequence show the algorithm can accurately track targets under small displacements but performance degrades for large displacements, fast motion, or occlusions. In conclusion, the mean shift algorithm provides a simple method for multi
Tag based image retrieval (tbir) using automatic image annotationeSAT Journals
Abstract In recent days, several social networking sites are more popular with digitized images. It comprises the major portion of the databases which makes the search engines to face difficulty in searching. We present a proficient image retrieval technique, which achieves eminent retrieval efficiency. Most of the images are annotated manually, thus the visual content and tags may be mismatched. This leads to poor performance in Tag Based Image Retrieval (TBIR). Automatic Image Annotation (AIA) analyzes the missing and noisy tags and over-refines it to increase the performance of TBIR. AIA can be achieved using the Tag Completion algorithm. The images retrieved from the TBIR are ranked based on the relevancy of the tags and visual content of the images. The relevancy can be evaluated using Content Based Image Retrieval (CBIR) technique. Based on the ranks, the images are indexed in the Tag matrix. Thus the images that match the search query can be retrieved in an optimal way. Keywords: Image Retrieval, Automatic Image Annotation, Tag Based Image Retrieval (TBIR), Tag Completion Algorithm, Content Based Image Retrieval (CBIR), Tag Matrix
IRJET - A Review on Text Recognition for Visually Blind PeopleIRJET Journal
This document reviews different techniques for text recognition to assist visually impaired people. It discusses past work that has used techniques like motion detection, optical character recognition (OCR), and template matching to detect and recognize text in images captured by cameras held by users. The document also reviews methods that convert recognized text to speech for output to users, like text-to-speech synthesis. It surveys several papers that have proposed systems combining these techniques or improving methods like OCR and text-to-speech. The review focuses on developing a text recognition system to enhance independent living for visually impaired users.
This document summarizes a research paper that proposes a feature level fusion based bimodal biometric authentication system using fingerprint and face recognition with transformation domain techniques. The system extracts fingerprint features using Dual Tree Complex Wavelet Transforms and extracts face features using Discrete Wavelet Transforms. It then concatenates the fingerprint and face features into a single feature vector. Euclidean distance is used to match test biometrics to those stored in a database. The proposed algorithm is shown to achieve better equal error rates and true positive identification rates compared to individual transformation domain techniques.
An Enhance Image Retrieval of User Interest Using Query Specific Approach and...IJSRD
In recent years, image retrieval process has increased artistically. An image retrieval system is a process for searching and retrieving images from large amount of the image dataset. Color, texture and edge have been the primitive low level image descriptors in content based image retrieval systems. In this paper we discover a system which splits the search process into two stages. In the query specify approach the feature descriptors of a query image we re-extracted and then used to check the similarity between the query image and those images which is in database. In the evolution stage, the most relevant images where retrieved by using the Interactive genetic algorithm. IGA help the users to retrieve the images that are most relevant to the users’ need and SVM will rank the image as their title and as par time of search. So that user can get search image as par their requirements.
Mri brain image retrieval using multi support vector machine classifiersrilaxmi524
This document discusses content-based image retrieval (CBIR) for medical images. It proposes using multiple query images instead of a single query image to improve retrieval accuracy. The system works by preprocessing queries, extracting features like texture from the queries, optimizing the features, using classifiers like SVM to categorize images, and then using KNN to retrieve similar images from the database based on feature matching. It claims this approach improves on existing CBIR systems that rely on annotations and have difficulties bridging the semantic gap between low-level features and high-level meanings.
Multivariate feature descriptor based cbir model to query large image databasesIJARIIT
The content based image retrieval (CBIR) applications have grown their popularity in the past decade with the
exponential growth in the image data volumes. The social networks have aggravated the size of image data on the internet. Social
network enables everyone to upload the images of one’s choice, which becomes the reason behind aggregation of millions of
images on the cyber space. It’s not possible to query these large image databases with the ordinary methods. Hence there was a
strong requirement of a smart and intelligent method to discover the similar images, which has been accomplished by using the
machine learning methods. In this paper, the multivariate feature descriptor method has been presented to extract the required
and relevant information from the large image databases. The proposed multivariate method involves the image color and texture
for the purpose of image matching to the query image (also known as a reference image). The most matching entities are returned
as the final results by the image extraction method. There are four methods, which involves three singular feature and one
multivariate feature based models, have been implemented. The multivariate model has been found much stable and returned
the maximum accuracy under this model.
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 Fast and Accurate Palmprint Identification System based on Consistency Orie...IJTET Journal
Abstract — A palmprint identification system is a relatively most promising physiological biometric approach to identify the person. The numbers of palmprint recognition based biometric system have been successfully applied for real world access to control applications. A typical palmprint identification system identifies a query palmprint and matching it with the template stored in the database and comparing the similarity score with a pre-defined threshold. The Consistency Orientation Pattern (COP) hashing method is implemented in this work to enforce the fast search and to obtain the accurate result. Orientation pattern (OP) is defined as a collection of orientation features at arbitrary positions. The principal palm line is a kind of evident and stable features in palmprint images, and the orientation features in this region are expected to be more consistent than others. Using the orientation and response features extracted by steerable filter and gives an analysis on the consistency of orientation features, and then introduces a method to construct COP using the consistent features. Those features can be used as the indexes to the target template. Because the COP is very stable across the samples of the same subject, the COP hashing method can find the target template quickly. This method can lead to early termination of the searching process.
Enhanced Thinning Based Finger Print RecognitionIJCI JOURNAL
This paper is the implementation of fingerprint recognition system in which the matching is done using the
Minutiae points. The methodology is the extracting & applying matching procedure on the Minutiae points
between the sample fingerprint & fingerprint under question. The main functional blocks of this system
follows steps of Image Thinning, Image Segmentation, Minutiae (feature) point Extraction, & Minutiae
point Matching. The procedure of Enhanced Thinning included for the purpose of decreasing the size of the
memory space used by the fingerprint image database.
Optical character recognition an encompassing revieweSAT Journals
Abstract
Optical character recognition (OCR) is becoming a powerful tool in the field of Character Recognition, now a days. In the existing globalized environment, OCR can play a vital role in different application fields. Basically, OCR technique converts images into editable format. This technique converts images in the form of documents such as we can edit, modify and store data more safely for longtime. This paper presents basic of OCR technique with its components such as pre-processing, Feature Extraction, Classification, post-processing etc. There are various techniques have been implemented for the recognition of character. This Review also discusses different ideas implemented earlier for recognition of a character. This paper may act as a supportive material for those who wish to know about OCR.
Keywords- OCR, Feature Extraction
IRJET- Deep Learning Techniques for Object DetectionIRJET Journal
The document discusses deep learning techniques for object detection in images. It provides an overview of convolutional neural networks (CNNs), the most popular deep learning approach for computer vision tasks. The document describes the basic architecture of CNNs, including convolutional layers, pooling layers, and fully connected layers. It then discusses several state-of-the-art CNN models for object detection, including ResNet, R-CNN, SSD, and YOLO. The document aims to help newcomers understand the key deep learning techniques and models used for object detection in computer vision.
Uncompressed Video Streaming in Wireless Channel without Interpolation using ...IJASRD Journal
This document summarizes an article that proposes methods for uncompressed video streaming over wireless channels using search algorithms without interpolation. It introduces full search and logarithmic search algorithms to estimate pixel motion for video transmission. The full search algorithm provides optimal estimation but is computationally expensive, while the logarithmic search algorithm reduces complexity. Experimental results show the full search algorithm achieves higher peak signal-to-noise ratio than existing methods. The proposed methods improve video quality for wireless transmission without increasing computational complexity.
This document summarizes a study that used neural networks and particle swarm optimization incorporating fuzzy c-means (PSOFCN) segmentation to recognize handwritten characters in the Meetei Mayek script. 34 characters were analyzed. Images were preprocessed, segmented using PSOFCN and recognized using a multilayer feedforward neural network with backpropagation. 1700 samples were used for training and 1700 for testing. Recognition accuracy ranged from 30-100%, with an average of 72%. Characters with simpler shapes had higher accuracy than more complex characters.
Text Recognition using Convolutional Neural Network: A ReviewIRJET Journal
This document reviews a system for text recognition using convolutional neural networks. The system uses an artificial neural network and nearest neighbor concepts to develop an optical character recognition (OCR) engine. The OCR engine takes images as input and converts them to soft copies through various processing stages, including preprocessing, segmentation, character recognition, and error detection and correction. It aims to improve on existing OCR engines by reducing errors. The system is intended to be implemented as an Android app to allow offline conversion of printed texts to soft copies. It reviews the methodology and various components of the proposed system, including the neural network architecture and training approach.
Multibiometric Secure Index Value Code Generation for Authentication and Retr...ijsrd.com
The use of multiple biometric sources for human recognition, referred to as multibiometrics, mitigates some of the limitations of unimodal biometric systems by increasing recognition accuracy, improving population coverage, imparting fault-tolerance, and enhancing security. In a biometric identification system, the identity corresponding to the input data (probe) is typically determined by comparing it against the templates of all identities in a database (gallery). An alternative e approach is to limit the number of identities against which matching is performed based on criteria that are fast to evaluate. We propose a method for generating fixed-length codes for indexing biometric databases. An index code is constructed by computing match scores between a biometric image and a fixed set of reference images. Candidate identities are retrieved based on the similarity between the index code of the probe image and those of the identities in the database. The number of multibiometric systems deployed on a national scale is increasing and the sizes of the underlying databases are growing. These databases are used extensively, thereby requiring efficient ways for searching and retrieving relevant identities. Searching a biometric database for an identity is usually done by comparing the probe image against every enrolled identity in the database and generating a ranked list of candidate identities. Depending on the nature of the matching algorithm, the matching speed in some systems can be slow. The proposed technique can be easily extended to retrieve pertinent identities from multimodal databases. Experiments on a chimeric face and fingerprint bimodal database resulted in an 84% average reduction in the search space at a hit rate of 100%. These results suggest that the proposed indexing scheme has the potential to substantially reduce the response time without compromising the accuracy of identification. New representation schemes that allow for faster search and, therefore, shorter response time are needed.
"The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content based image retrieval CBIR , which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research. Santosh Kumar Swarnkar | Prof. Avinash Sharma ""Content-Based Image Retrieval: An Assessment"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21708.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/21708/content-based-image-retrieval-an-assessment/santosh-kumar-swarnkar"
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.
IRJET-MText Extraction from Images using Convolutional Neural NetworkIRJET Journal
The document describes a text extraction model that uses convolutional neural networks (CNNs) to detect and recognize text in images. It discusses pre-processing techniques like binarization and filtering used to improve accuracy. A CNN based on ResNet18 architecture is used for text recognition, trained with CTC loss to handle variable-length text. Keywords can be searched for in extracted text and highlighted. The system allows browsing images, extracting text, searching text, and storing extracted text in an editable document format. While current technology can extract text from simple backgrounds, this model aims to handle more complex real-world images.
Text detection and recognition in scene images or natural images has applications in computer
vision systems like registration number plate detection, automatic traffic sign detection, image retrieval
and help for visually impaired people. Scene text, however, has complicated background, blur image,
partly occluded text, variations in font-styles, image noise and ranging illumination. Hence scene text
recognition could be a difficult computer vision problem. In this paper connected component method
is used to extract the text from background. In this work, horizontal and vertical projection profiles,
geometric properties of text, image binirization and gap filling method are used to extract the text from
scene images. Then histogram based threshold is applied to separate text background of the images.
Finally text is extracted from images.
IRJET- Real-Time Text Reader for English LanguageIRJET Journal
This document summarizes a research paper that presents a real-time text reader system for the English language. The system uses optical character recognition and support vector machines for text recognition and classification. It recognizes text from images, videos, and handwritten documents and classifies the text into predefined parts of speech categories for English. The system first detects text from the input source using OCR, then classifies and categorizes the recognized text.
Fingerprints are imprints formed by friction
ridges of the skin and thumbs. They have long been used for
identification because of their immutability and individuality.
Immutability refers to the permanent and unchanging character
of the pattern on each finger. Individuality refers to the
uniqueness of ridge details across individuals; the probability
that two fingerprints are alike is about 1 in 1.9x1015. In despite of
this improvement which is adopted by the Federal Bureau of
Investigation (FBI), the fact still is “The larger the fingerprint
files became, the harder it was to identify somebody from their
fingerprints alone. Moreover, the fingerprint requires one of the
largest data templates in the biometric field”. The finger data
template can range anywhere from several hundred bytes to over
1,000 bytes depending upon the level of security that is required
and the method that is used to scan one's fingerprint. For these
reasons this work is motivated to present another way to tackle
the problem that is relies on the properties of Vector
Quantization coding algorithm.
This document summarizes a research paper on tracking multiple targets using the mean shift algorithm. It begins by stating that multi-target tracking is challenging due to factors like noise, clutter, occlusions, and sudden changes in velocity. The mean shift algorithm is then introduced as a kernel-based tracking method that works by iteratively shifting target locations to their mean shifts. Targets are represented using histograms within elliptical regions. The Bhattacharyya coefficient is used to measure similarity between target models and candidates. Experimental results on a video sequence show the algorithm can accurately track targets under small displacements but performance degrades for large displacements, fast motion, or occlusions. In conclusion, the mean shift algorithm provides a simple method for multi
Tag based image retrieval (tbir) using automatic image annotationeSAT Journals
Abstract In recent days, several social networking sites are more popular with digitized images. It comprises the major portion of the databases which makes the search engines to face difficulty in searching. We present a proficient image retrieval technique, which achieves eminent retrieval efficiency. Most of the images are annotated manually, thus the visual content and tags may be mismatched. This leads to poor performance in Tag Based Image Retrieval (TBIR). Automatic Image Annotation (AIA) analyzes the missing and noisy tags and over-refines it to increase the performance of TBIR. AIA can be achieved using the Tag Completion algorithm. The images retrieved from the TBIR are ranked based on the relevancy of the tags and visual content of the images. The relevancy can be evaluated using Content Based Image Retrieval (CBIR) technique. Based on the ranks, the images are indexed in the Tag matrix. Thus the images that match the search query can be retrieved in an optimal way. Keywords: Image Retrieval, Automatic Image Annotation, Tag Based Image Retrieval (TBIR), Tag Completion Algorithm, Content Based Image Retrieval (CBIR), Tag Matrix
IRJET - A Review on Text Recognition for Visually Blind PeopleIRJET Journal
This document reviews different techniques for text recognition to assist visually impaired people. It discusses past work that has used techniques like motion detection, optical character recognition (OCR), and template matching to detect and recognize text in images captured by cameras held by users. The document also reviews methods that convert recognized text to speech for output to users, like text-to-speech synthesis. It surveys several papers that have proposed systems combining these techniques or improving methods like OCR and text-to-speech. The review focuses on developing a text recognition system to enhance independent living for visually impaired users.
This document summarizes a research paper that proposes a feature level fusion based bimodal biometric authentication system using fingerprint and face recognition with transformation domain techniques. The system extracts fingerprint features using Dual Tree Complex Wavelet Transforms and extracts face features using Discrete Wavelet Transforms. It then concatenates the fingerprint and face features into a single feature vector. Euclidean distance is used to match test biometrics to those stored in a database. The proposed algorithm is shown to achieve better equal error rates and true positive identification rates compared to individual transformation domain techniques.
An Enhance Image Retrieval of User Interest Using Query Specific Approach and...IJSRD
In recent years, image retrieval process has increased artistically. An image retrieval system is a process for searching and retrieving images from large amount of the image dataset. Color, texture and edge have been the primitive low level image descriptors in content based image retrieval systems. In this paper we discover a system which splits the search process into two stages. In the query specify approach the feature descriptors of a query image we re-extracted and then used to check the similarity between the query image and those images which is in database. In the evolution stage, the most relevant images where retrieved by using the Interactive genetic algorithm. IGA help the users to retrieve the images that are most relevant to the users’ need and SVM will rank the image as their title and as par time of search. So that user can get search image as par their requirements.
Mri brain image retrieval using multi support vector machine classifiersrilaxmi524
This document discusses content-based image retrieval (CBIR) for medical images. It proposes using multiple query images instead of a single query image to improve retrieval accuracy. The system works by preprocessing queries, extracting features like texture from the queries, optimizing the features, using classifiers like SVM to categorize images, and then using KNN to retrieve similar images from the database based on feature matching. It claims this approach improves on existing CBIR systems that rely on annotations and have difficulties bridging the semantic gap between low-level features and high-level meanings.
Multivariate feature descriptor based cbir model to query large image databasesIJARIIT
The content based image retrieval (CBIR) applications have grown their popularity in the past decade with the
exponential growth in the image data volumes. The social networks have aggravated the size of image data on the internet. Social
network enables everyone to upload the images of one’s choice, which becomes the reason behind aggregation of millions of
images on the cyber space. It’s not possible to query these large image databases with the ordinary methods. Hence there was a
strong requirement of a smart and intelligent method to discover the similar images, which has been accomplished by using the
machine learning methods. In this paper, the multivariate feature descriptor method has been presented to extract the required
and relevant information from the large image databases. The proposed multivariate method involves the image color and texture
for the purpose of image matching to the query image (also known as a reference image). The most matching entities are returned
as the final results by the image extraction method. There are four methods, which involves three singular feature and one
multivariate feature based models, have been implemented. The multivariate model has been found much stable and returned
the maximum accuracy under this model.
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 Fast and Accurate Palmprint Identification System based on Consistency Orie...IJTET Journal
Abstract — A palmprint identification system is a relatively most promising physiological biometric approach to identify the person. The numbers of palmprint recognition based biometric system have been successfully applied for real world access to control applications. A typical palmprint identification system identifies a query palmprint and matching it with the template stored in the database and comparing the similarity score with a pre-defined threshold. The Consistency Orientation Pattern (COP) hashing method is implemented in this work to enforce the fast search and to obtain the accurate result. Orientation pattern (OP) is defined as a collection of orientation features at arbitrary positions. The principal palm line is a kind of evident and stable features in palmprint images, and the orientation features in this region are expected to be more consistent than others. Using the orientation and response features extracted by steerable filter and gives an analysis on the consistency of orientation features, and then introduces a method to construct COP using the consistent features. Those features can be used as the indexes to the target template. Because the COP is very stable across the samples of the same subject, the COP hashing method can find the target template quickly. This method can lead to early termination of the searching process.
Enhanced Thinning Based Finger Print RecognitionIJCI JOURNAL
This paper is the implementation of fingerprint recognition system in which the matching is done using the
Minutiae points. The methodology is the extracting & applying matching procedure on the Minutiae points
between the sample fingerprint & fingerprint under question. The main functional blocks of this system
follows steps of Image Thinning, Image Segmentation, Minutiae (feature) point Extraction, & Minutiae
point Matching. The procedure of Enhanced Thinning included for the purpose of decreasing the size of the
memory space used by the fingerprint image database.
Optical character recognition an encompassing revieweSAT Journals
Abstract
Optical character recognition (OCR) is becoming a powerful tool in the field of Character Recognition, now a days. In the existing globalized environment, OCR can play a vital role in different application fields. Basically, OCR technique converts images into editable format. This technique converts images in the form of documents such as we can edit, modify and store data more safely for longtime. This paper presents basic of OCR technique with its components such as pre-processing, Feature Extraction, Classification, post-processing etc. There are various techniques have been implemented for the recognition of character. This Review also discusses different ideas implemented earlier for recognition of a character. This paper may act as a supportive material for those who wish to know about OCR.
Keywords- OCR, Feature Extraction
IRJET- Deep Learning Techniques for Object DetectionIRJET Journal
The document discusses deep learning techniques for object detection in images. It provides an overview of convolutional neural networks (CNNs), the most popular deep learning approach for computer vision tasks. The document describes the basic architecture of CNNs, including convolutional layers, pooling layers, and fully connected layers. It then discusses several state-of-the-art CNN models for object detection, including ResNet, R-CNN, SSD, and YOLO. The document aims to help newcomers understand the key deep learning techniques and models used for object detection in computer vision.
Uncompressed Video Streaming in Wireless Channel without Interpolation using ...IJASRD Journal
This document summarizes an article that proposes methods for uncompressed video streaming over wireless channels using search algorithms without interpolation. It introduces full search and logarithmic search algorithms to estimate pixel motion for video transmission. The full search algorithm provides optimal estimation but is computationally expensive, while the logarithmic search algorithm reduces complexity. Experimental results show the full search algorithm achieves higher peak signal-to-noise ratio than existing methods. The proposed methods improve video quality for wireless transmission without increasing computational complexity.
This document summarizes a study that used neural networks and particle swarm optimization incorporating fuzzy c-means (PSOFCN) segmentation to recognize handwritten characters in the Meetei Mayek script. 34 characters were analyzed. Images were preprocessed, segmented using PSOFCN and recognized using a multilayer feedforward neural network with backpropagation. 1700 samples were used for training and 1700 for testing. Recognition accuracy ranged from 30-100%, with an average of 72%. Characters with simpler shapes had higher accuracy than more complex characters.
Text Recognition using Convolutional Neural Network: A ReviewIRJET Journal
This document reviews a system for text recognition using convolutional neural networks. The system uses an artificial neural network and nearest neighbor concepts to develop an optical character recognition (OCR) engine. The OCR engine takes images as input and converts them to soft copies through various processing stages, including preprocessing, segmentation, character recognition, and error detection and correction. It aims to improve on existing OCR engines by reducing errors. The system is intended to be implemented as an Android app to allow offline conversion of printed texts to soft copies. It reviews the methodology and various components of the proposed system, including the neural network architecture and training approach.
Multibiometric Secure Index Value Code Generation for Authentication and Retr...ijsrd.com
The use of multiple biometric sources for human recognition, referred to as multibiometrics, mitigates some of the limitations of unimodal biometric systems by increasing recognition accuracy, improving population coverage, imparting fault-tolerance, and enhancing security. In a biometric identification system, the identity corresponding to the input data (probe) is typically determined by comparing it against the templates of all identities in a database (gallery). An alternative e approach is to limit the number of identities against which matching is performed based on criteria that are fast to evaluate. We propose a method for generating fixed-length codes for indexing biometric databases. An index code is constructed by computing match scores between a biometric image and a fixed set of reference images. Candidate identities are retrieved based on the similarity between the index code of the probe image and those of the identities in the database. The number of multibiometric systems deployed on a national scale is increasing and the sizes of the underlying databases are growing. These databases are used extensively, thereby requiring efficient ways for searching and retrieving relevant identities. Searching a biometric database for an identity is usually done by comparing the probe image against every enrolled identity in the database and generating a ranked list of candidate identities. Depending on the nature of the matching algorithm, the matching speed in some systems can be slow. The proposed technique can be easily extended to retrieve pertinent identities from multimodal databases. Experiments on a chimeric face and fingerprint bimodal database resulted in an 84% average reduction in the search space at a hit rate of 100%. These results suggest that the proposed indexing scheme has the potential to substantially reduce the response time without compromising the accuracy of identification. New representation schemes that allow for faster search and, therefore, shorter response time are needed.
"The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content based image retrieval CBIR , which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research. Santosh Kumar Swarnkar | Prof. Avinash Sharma ""Content-Based Image Retrieval: An Assessment"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21708.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/21708/content-based-image-retrieval-an-assessment/santosh-kumar-swarnkar"
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.
IRJET-MText Extraction from Images using Convolutional Neural NetworkIRJET Journal
The document describes a text extraction model that uses convolutional neural networks (CNNs) to detect and recognize text in images. It discusses pre-processing techniques like binarization and filtering used to improve accuracy. A CNN based on ResNet18 architecture is used for text recognition, trained with CTC loss to handle variable-length text. Keywords can be searched for in extracted text and highlighted. The system allows browsing images, extracting text, searching text, and storing extracted text in an editable document format. While current technology can extract text from simple backgrounds, this model aims to handle more complex real-world images.
Text detection and recognition in scene images or natural images has applications in computer
vision systems like registration number plate detection, automatic traffic sign detection, image retrieval
and help for visually impaired people. Scene text, however, has complicated background, blur image,
partly occluded text, variations in font-styles, image noise and ranging illumination. Hence scene text
recognition could be a difficult computer vision problem. In this paper connected component method
is used to extract the text from background. In this work, horizontal and vertical projection profiles,
geometric properties of text, image binirization and gap filling method are used to extract the text from
scene images. Then histogram based threshold is applied to separate text background of the images.
Finally text is extracted from images.
IRJET- Real-Time Text Reader for English LanguageIRJET Journal
This document summarizes a research paper that presents a real-time text reader system for the English language. The system uses optical character recognition and support vector machines for text recognition and classification. It recognizes text from images, videos, and handwritten documents and classifies the text into predefined parts of speech categories for English. The system first detects text from the input source using OCR, then classifies and categorizes the recognized text.
IRJET - Content based Image ClassificationIRJET Journal
The document discusses content based image classification, which involves grouping large numbers of digital images uploaded daily into categories based on their visual content. It describes how content based image classification systems work by extracting features from images like shape, color, and texture to classify them. The document also outlines some challenges in content based image classification and potential areas of future research like using deep learning approaches.
IRJET- Intelligent Character Recognition of Handwritten CharactersIRJET Journal
This document summarizes research on intelligent character recognition of handwritten characters using neural networks. It discusses how neural networks can be trained on feature vectors extracted from images to accurately recognize (up to 95%) handwritten alphanumeric characters. The proposed system segments images into characters, extracts features like intersections and endpoints, trains a neural network on feature vectors, and then uses the trained network to recognize new characters. It achieved high accuracy after training on a large dataset of 400 samples. The system automatically transfers recognized text to an Excel sheet.
Product Label Reading System for visually challenged peopleIRJET Journal
1) The document proposes a camera-based assistive text reading system to help blind people read text labels on handheld objects. It uses computer vision techniques like stroke width transform to isolate the object of interest and detect the region of interest.
2) In the region of interest, the system performs text localization using gradient features and edge distributions. It then recognizes text using optical character recognition and outputs it verbally for the user.
3) The system aims to achieve robust text extraction and recognition from complex backgrounds while focusing on usability. It analyzes existing assistive technologies and proposes an improved workflow including image capture, processing, and audio output.
IRJET- Detection and Recognition of Hypertexts in Imagery using Text Reco...IRJET Journal
The document discusses a technique for detecting and recognizing hyperlink text in images using text recognition. It proposes analyzing images on handheld devices to recognize patterns like URLs, emails, etc. and allow users to select them to open in a browser or app. It reviews related work on text recognition challenges. The proposed system would scan active images, recognize hyperlink patterns, and give users an option to select them and redirect to the appropriate destination. This could help simplify accessing links from product images and other media. Future work aims to build a more accurate model and implement the technique on a large set of images.
Scene Text Detection of Curved Text Using Gradiant Vector Flow MethodIJTET Journal
Abstract--Text detection and recognition is a hot topic for researchers in the field of image processing and multimedia. Content based Image Retrieval (CBIR) community fills the semantic gap between low-level and high-level features. For text detection and extraction that achieve reasonable accuracy for multi-oriented text and natural scene text (camera images), several methods have been developed. In general most of the methods use classifier and large number of training samples to improve the accuracy in text detection. In general, connected components are used to tackle the multi-orientation problem. The connected component analysis based features with classifier training, work well for achieving better accuracy when the images are highly contrast. However, when the same methods used directly for text detection in video it results in disconnections, loss of shapes etc, because of low contrast and complex background. For such cases, deciding geometrical features of the components and classifier is not that easy. To overcome from this problem the proposed research uses Gradiant Vector Flow and Grouping based Method for Arbitrarily Oriented Scene text Detection method. The GVF of edge pixels in the Sobel edge map of the input frame is explored to identify the dominant edge pixels which represent text components. The method extracts dominant pixel’s edge components corresponding to the Sobel edge map, which is called Text Candidates (TC) of the text lines. Experimental results on different datasets including text data that is oriented arbitrary, non-horizontal text data also horizontal text data, Hua’s data and ICDAR-03 data (Camera images) show that the proposed method outperforms existing methods.
IRJET- Image Caption Generation System using Neural Network with Attention Me...IRJET Journal
This document describes a system for generating image captions using neural networks with attention mechanisms. It involves using a convolutional neural network (CNN) as an encoder to extract image features, and a long short-term memory (LSTM) network as a decoder to generate words describing the image. An attention mechanism is used to provide more focus on important regions of the image. Optimal beam search is employed to construct optimal captions from the generated words. The system was developed to generate more descriptive captions and help visually impaired people understand images. It was evaluated on the Flickr8K dataset using BLEU score metrics.
Text Extraction of Colour Images using Mathematical Morphology & HAAR TransformIOSR Journals
This document summarizes a research paper that proposes a method to extract text from color images using the Haar discrete wavelet transform (DWT) and mathematical morphology. It begins with an introduction to text extraction challenges and prior approaches. The proposed method detects text edges using the Haar DWT and removes non-text edges with thresholding. Morphological dilation is then used to connect isolated candidate text edges. Mathematical morphology and templates are also used to extract characters from images. The simulation was carried out using MATLAB for image processing.
Inpainting scheme for text in video a surveyeSAT Journals
This document summarizes text detection and removal schemes for video sequences. It discusses two main phases - text detection and video inpainting. For text detection, it describes various visual feature extraction techniques like edge detection and texture analysis. It also discusses machine learning approaches like multilayer perceptrons and support vector machines. For inpainting, it discusses using wavelet transforms to approximate boundary data and fill in missing regions after text is removed. The goal is to restore occluded parts of video frames while maintaining spatial and temporal consistency.
A Survey on Portable Camera-Based Assistive Text and Product Label Reading Fr...IRJET Journal
This document presents a survey of existing portable camera-based assistive text and product label reading systems for blind persons. It proposes a new framework that uses motion-based targeting to isolate the object of interest and a novel text localization algorithm combining gradient features and edge pixel distributions to automatically locate and recognize text. The system is designed to help blind persons read text labels on hand-held objects in their daily lives through a portable camera, data processing components, and audio output via Bluetooth earpiece. It aims to provide better independent access to common objects like medication bottles than existing optical aids and screen readers.
IMAGE TO TEXT TO SPEECH CONVERSION USING MACHINE LEARNINGIRJET Journal
This document describes a study on converting images to text to speech using machine learning. The researchers developed a system that uses optical character recognition to extract text from images, then converts the text to speech. They achieved over 99% accuracy on their test dataset of over 1 million images. Their integrated system was able to accurately extract and convert text from various real-world images like street signs and menus. The system has potential to improve accessibility for people with visual impairments by allowing printed information to be converted to audio. Future work includes handling lower quality images and expanding the system to support additional languages and applications.
Text Extraction and Recognition Using Median FilterIRJET Journal
This document discusses a method for extracting and recognizing text from digital comic images. It begins with an introduction describing the challenges of text extraction from complex comic images. It then describes the specific method used, which includes preprocessing the image with a median filter to reduce noise, detecting text "balloons" using connected component labeling algorithms, and then applying optical character recognition with an image centroid concept to extract and recognize the text. The key aspects of the proposed method are preprocessing with median filtering for edge preservation, balloon detection using connected component labeling, and using image centroid zones for feature extraction in optical character recognition of the text.
A Survey On Thresholding Operators of Text Extraction In VideosCSCJournals
Video indexing is an important problem that has interested by the communities of visual information in image processing. The detection and extraction of scene and caption text from unconstrained, general purpose video is an important research problem in the context of content-based retrieval and summarization. In this paper, the technique presented is for detection text from frames video. Finding the textual contents in images is a challenging and promising research area in information technology. Consequently, text detection and recognition in multimedia had become one of the most important fields in computer vision due to its valuable uses in a variety of recent technical applications. The work in this paper consists using morphological operations for extract text appearing in the video frames. The proposed scheme well as preprocessing to differentiate among where it as the high similarity between text and background information. Experimental results show that the resultant image is the image with only text. The evaluated criteria are applied with the image result and one obtained bay different operator.
A Survey On Thresholding Operators of Text Extraction In VideosCSCJournals
The document describes a technique for text detection in video frames that uses morphological operations. It involves four phases: 1) image enhancement to improve edge detection, 2) edge extraction using morphological operations, 3) labeling connected text regions, and 4) extracting text using Hough transforms. The technique is evaluated using quantitative metrics like boundary precision-recall and volume precision-recall to measure the accuracy of detected text boundaries and regions compared to ground truth. Experimental results show the technique can accurately detect and extract text from video frames.
Text Extraction from Image Using GAMMA Correction Method.IRJET Journal
This document presents a method for extracting text from images using gamma correction. It begins with an abstract describing the challenges of text extraction from images and how gamma correction can help improve accuracy over existing methods. The introduction provides background on different types of images containing text and challenges of text extraction. It then summarizes previous related work on text extraction techniques. The document goes on to describe the proposed gamma correction method in detail, outlining three rules for estimating the optimal gamma value to enhance the image and facilitate text extraction. It concludes by noting the method will be experimentally tested. In summary, the document proposes a new gamma correction approach for text extraction from images and provides context on the problem and past approaches.
Deep Learning in Text Recognition and Text Detection : A ReviewIRJET Journal
The document discusses text detection and recognition using deep learning techniques. It begins with an introduction to deep learning and its use in optical character recognition (OCR). There are two main components of OCR - text detection, which locates text in an image, and text recognition, which identifies the text. Convolutional neural networks (CNNs) are effective for both tasks. The document then outlines the steps for text detection and recognition using deep learning, including data collection, preprocessing, feature extraction using CNNs, training and validating models on datasets, and testing the trained models on new data.
A Survey on Image retrieval techniques with feature extractionIRJET Journal
This document discusses content-based image retrieval techniques. It provides an overview of different image retrieval approaches, including text-based, content-based, and hybrid methods. Content-based image retrieval aims to retrieve images based on automatically extracted visual features like color, texture, and shape, rather than relying on textual metadata or keywords. The document reviews recent research that has improved content-based image retrieval performance, such as incorporating relevance feedback and focusing on local image regions rather than global features. It also proposes a new image retrieval model to further optimize existing techniques.
IRJET- Capsearch - An Image Caption Generation Based SearchIRJET Journal
This document describes a machine learning model called CapSearch that generates captions for images to enable image search based on the generated captions. The model was trained on the Flickr-8k dataset and uses a VGG16 CNN to extract image features, an LSTM to generate captions, and a Flask web app to enable image retrieval based on user search queries by comparing the queries to the generated captions. The implementation achieved promising results, accurately generating captions and retrieving relevant images, with opportunities identified to improve accuracy and enable custom datasets and REST API integration.
Similar to IRJET- Text Line Detection in Camera Caputerd Images using Matlab GUI (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
We have designed & manufacture the Lubi Valves LBF series type of Butterfly Valves for General Utility Water applications as well as for HVAC applications.
A high-Speed Communication System is based on the Design of a Bi-NoC Router, ...DharmaBanothu
The Network on Chip (NoC) has emerged as an effective
solution for intercommunication infrastructure within System on
Chip (SoC) designs, overcoming the limitations of traditional
methods that face significant bottlenecks. However, the complexity
of NoC design presents numerous challenges related to
performance metrics such as scalability, latency, power
consumption, and signal integrity. This project addresses the
issues within the router's memory unit and proposes an enhanced
memory structure. To achieve efficient data transfer, FIFO buffers
are implemented in distributed RAM and virtual channels for
FPGA-based NoC. The project introduces advanced FIFO-based
memory units within the NoC router, assessing their performance
in a Bi-directional NoC (Bi-NoC) configuration. The primary
objective is to reduce the router's workload while enhancing the
FIFO internal structure. To further improve data transfer speed,
a Bi-NoC with a self-configurable intercommunication channel is
suggested. Simulation and synthesis results demonstrate
guaranteed throughput, predictable latency, and equitable
network access, showing significant improvement over previous
designs
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Sachpazis_Consolidation Settlement Calculation Program-The Python Code and th...Dr.Costas Sachpazis
Consolidation Settlement Calculation Program-The Python Code
By Professor Dr. Costas Sachpazis, Civil Engineer & Geologist
This program calculates the consolidation settlement for a foundation based on soil layer properties and foundation data. It allows users to input multiple soil layers and foundation characteristics to determine the total settlement.
Open Channel Flow: fluid flow with a free surfaceIndrajeet sahu
Open Channel Flow: This topic focuses on fluid flow with a free surface, such as in rivers, canals, and drainage ditches. Key concepts include the classification of flow types (steady vs. unsteady, uniform vs. non-uniform), hydraulic radius, flow resistance, Manning's equation, critical flow conditions, and energy and momentum principles. It also covers flow measurement techniques, gradually varied flow analysis, and the design of open channels. Understanding these principles is vital for effective water resource management and engineering applications.
Particle Swarm Optimization–Long Short-Term Memory based Channel Estimation w...IJCNCJournal
Paper Title
Particle Swarm Optimization–Long Short-Term Memory based Channel Estimation with Hybrid Beam Forming Power Transfer in WSN-IoT Applications
Authors
Reginald Jude Sixtus J and Tamilarasi Muthu, Puducherry Technological University, India
Abstract
Non-Orthogonal Multiple Access (NOMA) helps to overcome various difficulties in future technology wireless communications. NOMA, when utilized with millimeter wave multiple-input multiple-output (MIMO) systems, channel estimation becomes extremely difficult. For reaping the benefits of the NOMA and mm-Wave combination, effective channel estimation is required. In this paper, we propose an enhanced particle swarm optimization based long short-term memory estimator network (PSOLSTMEstNet), which is a neural network model that can be employed to forecast the bandwidth required in the mm-Wave MIMO network. The prime advantage of the LSTM is that it has the capability of dynamically adapting to the functioning pattern of fluctuating channel state. The LSTM stage with adaptive coding and modulation enhances the BER.PSO algorithm is employed to optimize input weights of LSTM network. The modified algorithm splits the power by channel condition of every single user. Participants will be first sorted into distinct groups depending upon respective channel conditions, using a hybrid beamforming approach. The network characteristics are fine-estimated using PSO-LSTMEstNet after a rough approximation of channels parameters derived from the received data.
Keywords
Signal to Noise Ratio (SNR), Bit Error Rate (BER), mm-Wave, MIMO, NOMA, deep learning, optimization.
Volume URL: https://airccse.org/journal/ijc2022.html
Abstract URL:https://aircconline.com/abstract/ijcnc/v14n5/14522cnc05.html
Pdf URL: https://aircconline.com/ijcnc/V14N5/14522cnc05.pdf
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#adhocnetwork #VANETs #OLSRrouting #routing #MPR #nderesidualenergy #korea #cognitiveradionetworks #radionetworks #rendezvoussequence
Here's where you can reach us : ijcnc@airccse.org or ijcnc@aircconline.com
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.