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检索条件"任意字段=5th Conference on Optical Pattern Recognition"

共 2232 条记录，以下是81-90 订阅

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Research on face feature extraction algorithm based on OpenCV with optical enhancements 5

Research on face feature extraction algorithm based on OpenC...

引用

5th International conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： Zhang, Li Chen, Shuxi Yu, Anyue Liu, Yuxiang School of Yonyou Digital and Intelligence Nantong Institute of Technology Nantong China

ISBN: (纸本)9781510691667

In the current digital era, face recognition technology is of great significance. Its core lies in the extraction of facial key-point features, which provides a crucial data foundation for subsequent applications. this research focuses on the face feature extraction algorithm based on OpenCV and conducts an in - depth exploration of the relevant functional algorithms and implementation methods. In terms of algorithms, the principle of the AdaBoost algorithm and the working mechanism of the cascade classifier based on Haar - like features are elaborated in detail. the functional implementation covers aspects such as dataset collection and annotation, data pre-processing (including image grayscale processing and face region extraction), as well as the detection of the nose, eyes, and smile among the facial features with optical enhancement in selected environments. the experimental results show that each detection part has achieved certain recognition rate and recognition time results. the achievements of this research point out the direction for the subsequent optimization of face recognition technology, contribute to its further development and innovation in multiple fields, and help promote its application in more areas. © 2025 SPIE.

关键词： Image enhancement

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Temple Inscriptions recognition and Transliteration in Devanagari Script 2

Temple Inscriptions Recognition and Transliteration in Devan...

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2nd IEEE International conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Babu, B. Sathish Shetty, Sannidhi Agarwal, Anushka Sreerama, Sai Lahari Bhustali, Vaishnavi K. Sanjana, Sanka RV College of Engineering Department of Artificial Intelligence and ML Bengaluru India RV College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350347982

Ancient inscriptions, palm scripts, manuscripts, etc., have vital information about India's rich culture. recognition and understanding of these inscriptions have been challenging for epigraphers and professionals. the goal of the proposed research is to advance optical character recognition methods for archival Vatteluttu script inscriptions, which date back to the 4th or 5th century AD. this paper discusses a deep learning model to transliterate the ancient Tamil inscriptions (Vatteluttu Script), which can be extended further to other languages. the proposed work is beneficial to epigraphists, archaeological researchers, and the general public who are interested in this topic. the developed deep learning model has achieved an accuracy of 84.12%. © 2023 IEEE.

关键词： optical character recognition

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Live Demonstration: Efficient Organic Photodetector based Active Matrix Imager for Real-time optical Character recognition 5

Live Demonstration: Efficient Organic Photodetector based Ac...

引用

5th IEEE International conference on Artificial Intelligence Circuits and Systems, AICAS 2023

作者： Shan, Tong Li, Jun Hou, Xiao Huang, Peijin Guo, Xiaojun Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China

ISBN: (纸本)9798350332674

this live demonstration presents real-time character recognition using a portable system composed of an organic photodetectors-based imaging array and a smartphone. the high specific detectivity of the organic photodiode enables sensitive imaging with an ultra-low light intensity. Furthermore, a smartphone application using deep learning-based algorithm training has been applied for character recognition. © 2023 IEEE.

关键词： Deep learning

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A Model For IC Defect Identification Based on Feature Extraction 5

A Model For IC Defect Identification Based on Feature Extrac...

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5th IEEE Eurasia conference on IOT, Communication and Engineering, ECICE 2023

作者： Chen, Chien-Chang Chen, You-Lin Lee, Jeng-Dao National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung Taiwan National Formosa University Department of Automation Engineering Yunlin Taiwan National Formosa University Smart Machinery and Intelligent Manufacturing Research Center Department of Electrical Engineering Yunlin Taiwan

ISBN: (纸本)9798350314694

We investigated the defect detection of components (ICs) on printed circuit boards (PCBs) in automated production lines. Due to the increasing miniaturization and higher density of components on PCBs along with the complexity and diversity of ICs, detecting and identifying defects in these electronic components is a critical issue. We proposed a defect recognition model based on feature extraction for ICs and an automatic optical inspection system (AOI). the research methodology consisted of five steps: data collection, IC localization, IC correction, feature extraction, and defect recognition. Data was collected in two parts: circuit board data and IC data. For IC localization, YOLOv4 was used to locate ICs on the circuit board and zoom in, magnifying them for easier defect feature extraction and reducing background noise. For IC correction, a horizontal projection correction method was used to analyze variations in black pixels among different angles in the image to determine and correct the horizontal tilt angle of the IC. Feature extraction was achieved by using image annotation tools to outline the regions of pin defects. Defect recognition involved training a YOLOv4 model on 1500 images and then using an additional 200 test images to evaluate the model's performance. the region recognition rate reached 98.2%, and after comparing the recognized defects with actual defects, the recognition accuracy reached 96.2%. © 2023 IEEE.

关键词： Automatic optical inspection horizontal projection integrated circuit component solder joint YOLOv4

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A Novel Machine Learning-based Equalizer for a Downstream 100G PAM-4 PON

A Novel Machine Learning-based Equalizer for a Downstream 10...

引用

optical Fiber Communications conference and Exhibition (OFC)

作者： Shao, Chen Giacoumidis, Elias Li, Shi Li, Jialei Faerber, Michael Kaefer, Tobias Richter, Andre Karlsruhe Inst Technol KIT Kaiserstr 12 D-76131 Karlsruhe Germany VPIphoton GmbH Hallerstr 6 D-10587 Berlin Germany Rhein Westfal TH Aachen Templergraben 57 D-52062 Aachen Germany

A frequency-calibrated SCINet (FC-SCINet) equalizer is proposed for downstream 100G PON with 28.7 dB path loss. At 5 km, FC-SCINet improves the BER by 88.87% compared to FFE and a 3-layer DNN with 10.57% lower complex... 详细信息

ISBN: (纸本)9798350377583

关键词： Bit error rate Neural networks optical networks Passive optical networks pattern recognition Signal processing

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5th EAI International conference on Emerging Technologies in Computing, iCETiC 2022

5th EAI International Conference on Emerging Technologies in...

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5th EAI International conference on Emerging Technologies in Computing, iCETiC 2022

ISBN: (纸本)9783031251603

the proceedings contain 10 papers. the special focus in this conference is on Emerging Technologies in Computing. the topics include: Evaluation of Semantic Interoperability of Automotive Service API Models Based on Metamodels Similarity Metrics Using a Semi-automated Approach;BCoT-Based Smart Manufacturing: An Enhanced Precise Measurement Management System;investigation of the Digitalisation Process of Traditional Businesses and Its Concept for Retail;data Integrity for Dynamic Big Data in Cloud Storage: A Comprehensive Review and Critical Issues;A Review of optical Character recognition (OCR) Techniques on Bengali Scripts;Traffic Sign Detection and recognition System Using Improved YOLOV5s;prediction of the Arbutus Unedo Colonization Time via an Agent-Based Distribution Model;stock Price Prediction Using Semi-supervised Ridge Regression.

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Noise Impact recognition in Fiber-Optic Construct Materials by Machine Learning Approach 5

Noise Impact Recognition in Fiber-Optic Construct Materials ...

引用

5th IEEE International conference on Communications, Information, Electronic and Energy Systems, CIEES 2024

作者： Balabanova, Ivelina Petrova, Desislava Georgiev, Georgi Technical University of Gabrovo Dept. of Communications Equipment and Technologies Gabrovo Bulgaria Technical University of Gabrovo Dept. of Managementt Gabrovo Bulgaria

ISBN: (纸本)9798350352863

the research examines one of the main problems of ensuring the performance of the transmission medium in the structure of optical routes. the main aspect is consist of the establishment of the noise impact types occurring in the construct materials of optical transmission lines. Gaussian White Noise and Periodic Random Noise in composite Glass and Plastic Optic Fibers were introduced as research objects. Regarding the identification processes of the target noises, an approach based on Machine Learning Methods was created. Different Machine Learning approaches were analyzed in STATISTICA and MATLAB platforms. With the highest confidence degree according to the achieved quantitative and qualitative assessment, Support Vector Machine, k-Nearest Neighbors, Adaptive Neuro-Fuzzy Interface Systems, Cascade-Forward Neural Networks and Probabilistic Neural Networks. the final synthesis of classification models is tied to the obtained maximum indices of Noise recognition, respectively 85.000 %, 87.100 %, 97.900 %, 99.06166 %, 99.700 % and 100.000 % using the Machine Leaning methods defined. © 2024 IEEE.

关键词： Plastic optical fibers

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

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

引用

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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Enhanced Camouflage Detection Using Advanced Image Processing Techniques for Real-Time Object recognition and pattern Differentiation 5

Enhanced Camouflage Detection Using Advanced Image Processin...

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

作者： Manikandan, G. Deepa, R. Jayalakshmi, V. thilakavathy, P. Surendran, R. St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

the identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. this finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. this dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast;nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. this research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. the LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. the approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. the recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection. © 2024 IEEE.

关键词： Accuracy Image processing Surveillance Image edge detection Wildlife Lighting Object detection Real-time systems Object recognition Protection

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Handwritten Character recognition for South Indian Languages Using Deep Learning 1

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5th International conference on Computer and Communication Technologies, IC3T 2023

作者： Arjun, C.S. Rani, N. Shobha Prabhu, Akshatha Department of Computer Science School of Computing Mysuru Campus Amrita Vishwa Vidyapeetham Mysuru India

ISBN: (数字)9789819997046

ISBN: (纸本)9789819997039

Character recognition is a crucial component of many applications, including document processing, handwriting recognition, and optical character recognition (OCR). We present an OCR model-based character recognition workflow in this research that involves feature extraction from the input images, model comparison, and performance assessment. We compared the performance of four OCR models, including FAST R-CNN, Efficient Net B7, VGG16, and VGG19, on Kannada and Malayalam handwritten character datasets. the results showed that Efficient Net B7 outperformed the other models with the highest accuracy. We also analysed the performance of each model in terms of precision, recall, and F1-score. Our results showed that FAST R-CNN was second with accuracy of 94.55%, Efficient Net B7 was first with accuracy of 96.66%, VGG19 was third with accuracy of 92.23%, and VGG16 was last with accuracy of 91.38%. the workflow provides a structured approach for developing and evaluating OCR models for character recognition tasks in Indian scripts. the findings can be useful for various applications that require accurate and efficient character recognition. Future work could focus on exploring other deep learning architectures and datasets for character recognition in Indian scripts. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： optical character recognition

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