scholarly journals Medial Representation Based Font Recognition Method

2020 ◽  
pp. 118-129
Author(s):  
A. Lipkina ◽  
L. Mestetskiy
Keyword(s):  
Mathematical Model ◽  
Recognition System ◽  
High Accuracy ◽  
Learning Problems ◽  
Text Recognition ◽  
Font Size ◽  
Recognition Method ◽  
Width Function ◽  
Font Recognition ◽  
Medial Representation

In this article, a method of font recognition based on the medial representation, integrated into the font recognition system based on a digital image of text is described. This system searches for similar fonts, ordered by similarity, to the font shown in the user-entered text image. The system is based on solving two machine learning problems: text recognition on an image and font recognition on a text image. To solve the first problem, we use the concept of a mathematical model of a grapheme based on a continuous medial representation of a symbol. The solution to the font recognition problem is based on the concept of the morphological width of the figure, which is also closely related to the medial representation. We propose a method for using the morphological width function to find the most similar fonts from a known database. The experiments show high accuracy of searching for the most similar fonts. For a database consisting of 2543 fonts, the accuracy is 0.991 according to the metric top@5 for correctly recognized text in the font size of 100 pixels in the image.

scholarly journals RESEARCH AND DEVELOPMENT OF TEXT RECOGNITION SYSTEM

2020 ◽  
Author(s):  
Oleksii Denysenko
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Character Recognition ◽  
High Reliability ◽  
Recognition System ◽  
Text Recognition ◽  
Light Conditions ◽  
Recognition Method ◽  
Low Light ◽  
Series Of Experiments

The paper discusses the technology of creating character recognition (using convolutional neural networks) systems on the image. These days, there are many approaches to solving this problem, and most of them are ineffective for images whose symbols are located on a complex background and are vulnerable to noise, affine and projection distortions. The proposed technique consists of the following stages: image pre-processing, text segmentation, and recognition by convolutional neural networks. During research was conducted a series of experiments, namely: experiment to select the most suitable method of binarization of digital images, experiment to select the most efficient convolutional neural network topology form text recognition problem. As a result of the experiments performed, this technique as applied to the recognition of car numbers demonstrates high reliability and accuracy, including in low light conditions, therefore, the developed recognition method can be recommended for commercial use. As an additional field of experiments was suggested a bunch of approaches of how to improve this technique.

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

scholarly journals Arabic Cursive Text Recognition from Natural Scene Images

2019 ◽  
Vol 9 (2) ◽  
pp. 236 ◽  
Author(s):  
Saad Ahmed ◽  
Saeeda Naz ◽  
Muhammad Razzak ◽  
Rubiyah Yusof
Keyword(s):  
Recognition System ◽  
Document Image ◽  
Text Recognition ◽  
Chinese Script ◽  
Challenging Problem ◽  
Future Directions ◽  
Scene Text ◽  
Comprehensive Survey ◽  
Recognition Systems ◽  
Scene Text Recognition

This paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years’ publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due to joined writing, same character variations, a large number of ligatures, the number of baselines, etc. Surveys on the Latin and Chinese script-based scene text recognition system can be found, but the Arabic like scene text recognition problem is yet to be addressed in detail. In this manuscript, a description is provided to highlight some of the latest techniques presented for text classification. The presented techniques following a deep learning architecture are equally suitable for the development of Arabic cursive scene text recognition systems. The issues pertaining to text localization and feature extraction are also presented. Moreover, this article emphasizes the importance of having benchmark cursive scene text dataset. Based on the discussion, future directions are outlined, some of which may provide insight about cursive scene text to researchers.

Research and Implementation of Face Detection Based on OpenCV

2014 ◽  
Vol 971-973 ◽  
pp. 1710-1713
Author(s):  
Wen Huan Wu ◽  
Ying Jun Zhao ◽  
Yong Fei Che
Keyword(s):  
Face Recognition ◽  
Detailed Analysis ◽  
Real Time ◽  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Accuracy Rate ◽  
Face Recognition System ◽  
The Face

Face detection is the key point in automatic face recognition system. This paper introduces the face detection algorithm with a cascade of Adaboost classifiers and how to configure OpenCV in MCVS. Using OpenCV realized the face detection. And a detailed analysis of the face detection results is presented. Through experiment, we found that the method used in this article has a high accuracy rate and better real-time.

Multi-Object Multi-Discipline Probabilistic Design of High-Pressure Turbine Blade-Tip Radial Running Clearance

2013 ◽  
Vol 572 ◽  
pp. 551-554
Author(s):  
Wen Zhong Tang ◽  
Cheng Wei Fei ◽  
Guang Chen Bai
Keyword(s):  
Mathematical Model ◽  
High Pressure ◽  
Optimal Design ◽  
Mechanical System ◽  
High Accuracy ◽  
Probabilistic Design ◽  
High Pressure Turbine ◽  
Surface Method ◽  
Blade Tip ◽  
The Mathematical Model

For the probabilistic design of high-pressure turbine (HPT) blade-tip radial running clearance (BTRRC), a distributed collaborative response surface method (DCRSM) was proposed, and the mathematical model of DCRSM was established. From the BTRRC probabilistic design based on DCRSM, the static clearance δ=1.865 mm is demonstrated to be optimal for the BTRRC design considering aeroengine reliability and efficiency. Meanwhile, DCRSM is proved to be of high accuracy and efficiency in the BTRRC probabilistic design. The present study offers an effective way for HPT BTRRC dynamic probabilistic design and provides also a promising method for the further probabilistic optimal design of complex mechanical system.

scholarly journals OCR with the Deep CNN Model for Ligature Script-Based Languages like Manchu

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Diandian Zhang ◽  
Yan Liu ◽  
Zhuowei Wang ◽  
Depei Wang
Keyword(s):  
Recognition Accuracy ◽  
Sliding Window ◽  
Recognition System ◽  
Text Retrieval ◽  
Text Recognition ◽  
Manual Segmentation ◽  
Low Resource ◽  
Sample Data ◽  
Deep Cnn

Manchu is a low-resource language that is rarely involved in text recognition technology. Because of the combination of typefaces, ordinary text recognition practice requires segmentation before recognition, which affects the recognition accuracy. In this paper, we propose a Manchu text recognition system divided into two parts: text recognition and text retrieval. First, a deep CNN model is used for text recognition, using a sliding window instead of manual segmentation. Second, text retrieval finds similarities within the image and locates the position of the recognized text in the database; this process is described in detail. We conducted comparative experiments on the FAST-NU dataset using different quantities of sample data, as well as comparisons with the latest model. The experiments revealed that the optimal results of the proposed deep CNN model reached 98.84%.

scholarly journals Convolutional Neural Network for Iris Recognition

2021 ◽  
Author(s):  
Wael Alnahari
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Iris Recognition ◽  
Recognition System ◽  
High Accuracy ◽  
Accuracy Rate

Abstract In this paper, I proposed an iris recognition system by using deep learning via neural networks (CNN). Although CNN is used for machine learning, the recognition is achieved by building a non-trained CNN network with multiple layers. The main objective of the code the test pictures’ category (aka person name) with a high accuracy rate after having extracted enough features from training pictures of the same category which are obtained from a that I added to the code. I used IITD iris which included 10 iris pictures for 223 people.

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