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Webcam-Based OCR System with Text-to-Speech Conversion 5

Webcam-Based OCR System with Text-to-Speech Conversion

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5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Poornima, J. Sambasivudu, Madduru Bhargavi, Manchikalapudi Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Department of Computer Applications Mangalore India Brindavan College of Engineering Department of Information Science and Engineering Bengaluru India Malla Reddy College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331530013

The project develops an OCR system that converts webcam-captured text to speech, featuring user registration for personalized experiences. Key components include image capture via webcam, text extraction using OCR, database storage, and text-to-speech conversion. The system employs Python, OpenCV for image processing, Tesseract for OCR, and Google Text-to-Speech for audio output. Main objectives are to streamline text digitization and assist visually impaired users. The modular design allows for future enhancements like language translation. Comprehensive testing ensures reliability and effectiveness. The system demonstrates an efficient solution for converting printed text to digital and audio formats, with applications in document digitization, education, and accessibility services. It reduces manual effort in text digitization and improves access to written information for visually impaired individuals, showcasing potential across various fields. © 2024 ieee.

关键词： Optical character recognition

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Review Paper on Centroiding Algorithm for Lunar Navigation 21

Review Paper on Centroiding Algorithm for Lunar Navigation

引用

21st ieee international Colloquium on signal processing and Its applications, CSPA 2025

作者： Razaman, Anis Hannani Ahmad, Yasser Asrul Khalifa, Othman Omran Saidin, Norazlina Azman, Amelia Wong International Islamic University Malaysia Department of Electrical and Computer Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798331522193

This paper reviews on potential algorithms for navigation on the moon's surface, focusing on centroiding algorithms in star sensors or trackers. Accurate positioning is crucial for lunar missions since there's no Global Positioning System (GPS), and navigation depends on pre-installed maps and sensors like Light Detection and Ranging (LiDAR) and star trackers. Star sensors or trackers are ideal for these missions due to their high accuracy. However, the moon's environment adds challenges, like interference from bright reflections and noise from radiation. This paper reviews different star-sensing methods, comparing CMOS and CCD sensors, and assesses their algorithms for their effectiveness in lunar mission. The research gap which are the strengths and weaknesses of these methods were discussed and identify areas for improvement. By addressing these gaps, this study aims to contribute to the development of more robust and accurate centroiding algorithms, advancing lunar navigation technologies for future exploration missions. © 2025 ieee.

关键词： Centroiding Algorithm CMOS image Extraction Lunar navigation Star Sensor

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Spatial Maturity Regression for the Classification of Hematopoietic Cells 11

Spatial Maturity Regression for the Classification of Hemato...

引用

11th international conference on image processing Theory, Tools and applications (IPTA)

作者： Graebel, Philipp Thull, Julian Crysandt, Martina Klinkhammer, Barbara M. Boor, Peter Bruemmendorf, Tim H. Merhof, Dorit Rhein Westfal TH Aachen Inst Imaging & Comp Vis Aachen Germany Rhein Westfal TH Aachen Univ Hosp Dept Hematol Oncol Hemostaseol & Stem Cell Transp Aachen Germany Rhein Westfal TH Aachen Univ Hosp Inst Pathol Aachen Germany

ISBN: (纸本)9781665469647

In contrast to peripheral blood, cells in bone marrow microscopy images are not only characterized by the cell lineage but also a maturity stage within the lineage. As maturation is a continuous process, the differentiation between various stages falls into the category of (ordinal) regression. In this work, we propose Spatial Maturity Regression - a technique that regularizes the learning process to enforce a sensible positioning of maturity stages in the embedding space. To this end, we propose and evaluate several curve models, target definitions and loss function that incorporate this domain knowledge. We show that the classification F-scores improve up to 2.4 percentage points when enforcing regression targets along learnable curves in the embedding space. This technique further allows visualization of individual predictions by providing the projected position along the learnt curve.

关键词： Visualization Microscopy image processing Neural networks Cells (biology) Bones Blood

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Analysis and Study of Conventional and Advanced image Segmentation Techniques for Machine Learning-Based Leaf Disease Detection and Classification 4

Analysis and Study of Conventional and Advanced Image Segmen...

引用

4th ieee international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： John, Sreya Rose, P. J. Arul Leena FSH SRMIST KTR Dept of Computer Science Chennai India

ISBN: (纸本)9798350306118

The demand for agricultural crops is constantly rising throughout time. This increment in demand is coupled with many other challenging factors such as climate change, pollution, urbanization etc that affects the quality and quantity of the crop production which can even lead to an economic breakdown. Lately, many studies were done to put an end to the difficulties faced by the agricultural sector. The application of technology in this area has been a pivotal turning point in the conservation of agricultural crops. Consequently, many computer based automated systems were developed to ease the tasks of farmers. Among these innovations, the application of image processing and machine learning has emerged as a breakthrough. By enabling early detection of plant diseases, these tools significantly contribute to maintaining crop health. Segmentation is an important stage in image processing, that is critical in accurately extracting relevant information from images. In this paper, we delve into the significance and advancements of segmentation, highlighting its major role in various applications. Additionally, we have also done a comparative study based on few experiments and observations in the latest improvements of various segmentation techniques. © 2023 ieee.

关键词： Machine learning

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LSR++: An Efficient and Tiny Model for image Super-Resolution

LSR++: An Efficient and Tiny Model for Image Super-Resolutio...

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2023 Asia Pacific signal and Information processing Association Annual Summit and conference, APSIPA ASC 2023

作者： Wang, Wei Lei, Xuejing Chen, Yueru Lee, Ming-Siu Kuo, C.-C. Jay University of Southern California Los AngelesCA United States PengCheng Laboratory Shenzhen China National Taiwan University Taipei Taiwan

ISBN: (纸本)9798350300673

A single-image lightweight super-resolution (LSR) method was presented in our earlier work. LSR predicts the residual image between the interpolated low-resolution (ILR) and high-resolution (HR) images using a self-supervised framework. A further enhanced version of LSR, called LSR++, is proposed in this work. The improvement of LSR++ over LSR primarily lies in a new patch alignment scheme, where the spatially geometrical property of patches is exploited. Patches can be categorized into two main classes through alignment. Then, one regressor is needed for each class. Compared with LSR, LSR++ lowers the computational complexity from 3.83K to 1.71K FLOPs/pixel and the model size from 770K to 200K parameters while preserving close visual performance measured by PSNR/SSIM. LSR++ offers an attractive super-resolution method for mobile applications. © 2023 ieee.

关键词： Optical resolving power

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Efficient signal processing: Harnessing Distributed Arithmetic for High-Speed FFT Operations 3

Efficient Signal Processing: Harnessing Distributed Arithmet...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Bharathi, M. Mohanarangam, Krithikaa Shirur, Yasha Jyothi M. Andhra Pradesh Tirupati India Symbiosis Institute of Technology Pune India Bnm Institute of Technology Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798331515911

The present article introduces a novel approach to significantly enhance the computational efficiency of modern Digital signal processing (DSP) processors. The algorithm enhances the efficiency of Sum of Products (SOP) operations, which are essential for real-time signal processing applications, by utilizing Distributed Arithmetic (DA) techniques for the Fast Fourier Transform (FFT) and its inverse (Inverse FFT) (IDFT) computations. The present study undertakes an exhaustive quantitative analysis to assess the computational delays of the proposed 2, 4, and 8-point Fast Fourier Transform (FFT) and Inverse Discrete Fourier Transform (IDFT) across various bit representations (4, 8, and 16-bit). For example, when using 4-bit representations, the computational delays for a 2-point Fast Fourier Transform (FFT) are measured to be 13.318ns, 30.629ns for a 4-point FFT, and 27.779ns for an 8-point FFT. Similarly, the delays for the 2-point Fast Fourier Transform (FFT) in an 8-bit representation are 13.727ns, the 4-point FFT in 22.276ns, and the 8-point FFT in 30.592ns. The study further includes the addition of appropriate delays for IDFT operations, which enhances the understanding of the effectiveness of the approach suggested in various scenarios. Furthermore, the research presents and evaluates three distinct categories of transforms, namely 2-point, 4-point, and 8-point FFT and IDFT structures. These structures utilize different quantities of Look-Up Tables (LUTs) for computational purposes. The research showcases the exceptional computing efficiency of the suggested design through detailed quantitative analysis, emphasizing its potential advantages in improving DSP implementations in practical situations. © 2025 ieee.

关键词： Inverse problems

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C-ESRGAN: Synthesis of super-resolution images by image classification 5

C-ESRGAN: Synthesis of super-resolution images by image clas...

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5th ieee international conference on image processing applications and Systems (IPAS)

作者： Liu, Jingan Chandrasiri, Naiwala P. Kogakuin Univ Intelligent Media Lab Tokyo Japan

ISBN: (纸本)9781665462198

With the development of deep learning, super-resolution image synthesis techniques for enhancing low-resolution images have advanced remarkably. However, mainstream algorithms focus on improving the quality of the entire image on average and this may result in blurring. In this paper, we propose three key components for synthesizing super-resolution images that can reflect the fine details of an image. We synthesize super-resolution images by image classification. First, the neural network weights learned using the images in the same image category were utilized in synthesizing super-resolution images. For this purpose, image classification was performed using a transfer-trained ResNet. Second, SENet was applied to the generators in our proposed method to obtain detailed information about the images. Finally, the feature extraction network was changed from VGG to ResNet in order to get more important features. As a result, we achieved better image evaluation values (PSNR, NIQE) for the super-resolution images of dogs and cats compared to the previous studies. Furthermore, the images were generated more naturally on the benchmark dataset.

关键词： Super-Resolution Generative Adversarial Networks (GANs) SENet image Classification ResNet

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Fluorescence image Denoising Based on Self-supervised Deep Learning 7

Fluorescence Image Denoising Based on Self-supervised Deep L...

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7th international conference on Intelligent Computing and signal processing, ICSP 2022

作者： Huang, Haojian Liu, Yixuan Li, Yang College of Computer Science and Technology Harbin Engineering University Harbin Heilongjiang150001 China College of Transportation Engineering Tongji University Shanghai201804 China College of Electrical Information Wuhan Institute of Technology Wuhan430200 China

ISBN: (纸本)9781665478571

Fluorescence microscopy imaging technology is a crucial imaging technology widely used in biomedical fields such as brain science. However, its images often have random noise without a fixed pattern, causing a series of problems such as image degradation, image segmentation, and classification misalignment. In order to obtain higher signal-to-noise ratio imaging, the input of higher doses of photons increases the cost and causes adverse effects such as photobleaching, phototoxicity, or tissue heating. Therefore, researchers have studied many traditional denoising methods and deep learning-based denoising methods. Traditional denoising methods have the advantage of fast and straightforward processing and tend to perform well when the noise level of the image is low, or the noise distribution is known. However, its effectiveness and generalization are minimal when the noise level is high and randomly distributed without a fixed modality. On the contrary, image denoising methods based on self-supervised deep learning can extract high-dimensional features of images, have better effectiveness and robustness in practical denoising applications. Morever, these methods reduce the need for manual annotation of large-scale data, making timely research on cutting- edge pathology possible when there are insufficient labeled data. In this paper, to tackle the denoising problem of fluorescence microscopy images, two classic traditional denoising methods, Wiener filtering, and wavelet transformation, and methods based on deep learning, DeepCAD, are used for comparative experiments. The experimental results show that the deep learning method has good effectiveness and generalization in dealing with high-level random noise, and it performs better than traditional denoising methods. © 2022 ieee.

关键词： Wavelet transforms

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A Comparative Analysis of image processing and Deep Learning Techniques for License Plate Recognition in Difficult Environments 5

A Comparative Analysis of Image Processing and Deep Learning...

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5th international conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Tejaswi, S. Babu, Tina Tejaswi, K. Seshadripuram Degree College Department of Computer Science Mysuru India School of Engineering Department of Computer Science and Engineering Alliance University Bengaluru India JSS Science and Technology University Department of Computer Applications Mysuru India

ISBN: (纸本)9798331518097

License plate recognition is one of the challenging tasks and it belongs to ITS, due to backgrounds, variation of illumination, occlusion the recognition is became the challenging task. These challenges enabled a comparison between the conventional image enhancement and the deep learning scheme for license plate recognition. We analyze and compare multiple techniques that we consider significant, including, the Edge detection, Morphological operations and template matching and also deep learning models like CNNs and R-CNNs. In this framework of experiment scenarios, we scale out a comprehensive evaluation of the methods given different datasets and compare their accuracy, speed as well as stability in conditions that may be hostile. In the experiments, we see that Naive Bayes and SVM outperform in low-resource conditions, but deep learning methods are significantly more accurate and more flexible in accommodating complicated cases and trends, which makes deep learning methods the method of choice for contemporary applications. This work provides an understanding of the strengths and weaknesses of both approaches to serve as a guideline in choosing the most effective detection techniques in relation to certain environmental conditions. © 2024 ieee.

关键词： Intelligent systems

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Fusion-based Digital Twin Framework with Convolutional Neural Networks (CNN) for Real-Time Threat Detection and image Enhancement in Low-Light IoT Environments 2

Fusion-based Digital Twin Framework with Convolutional Neura...

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2nd ieee international conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Kumar, K. Ashok Barmola, Pareshwar Prasad Velmurugan, S.P. Manjunath, A.C. Soni, Umang Maranan, Ramya Department of Physics B V Raju Institute of Technology Medak Narsapur India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishankoil India Department of Mechanical Engineering Brindavan college of Engineering Bangalore India Faculty of Engineering and Technology Parul institute of Engineering and Technology Vadodara India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9798331504960

image acquisition in low-light environments often suffers from poor contrast, high noise, and loss of fine details, making effective enhancement challenging. Conventional enhancement methods frequently fail to balance detail preservation and noise suppression. This paper introduces a novel fusion-based technique for enhancing images captured in low-light settings. The proposed method integrates multi-scale decomposition, adaptive histogram equalization, and wavelet transform to improve image quality. Multi-scale decomposition captures critical structural information, adaptive histogram equalization adjusts intensity levels for enhanced contrast, and wavelet transform reduces noise while refining image details. Evaluation on standard low-light image datasets demonstrates that this method outperforms current state-of-the-art techniques. The enhanced images show a peak signal-to-noise ratio (PSNR) of 32.45 dB and a structural similarity index (SSIM) of 0.87, reflecting an 18% improvement in PSNR and a 12% gain in SSIM over traditional methods. Additionally, the technique reduces noise variance by 20% and enhances visual clarity, making it suitable for applications such as surveillance, medical imaging, and autonomous driving. In conclusion, this fusion-based approach provides an effective solution for low-light image enhancement, maintaining critical details and achieving improved visual quality through balanced contrast enhancement and noise reduction. © 2024 ieee.

关键词： Wavelet decomposition

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