scholarly journals Authorized Vehicle Recognition System

2020 ◽  
Author(s):  
Kedar R ◽  
Kaviraj A ◽  
Manish R ◽  
Niteesh B ◽  
Suthir S
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Recognition System ◽  
Learning Object ◽  
Image Search ◽  
Human Beings ◽  
Government Organizations ◽  
Vehicle Recognition ◽  
Character Extraction ◽  
Region Extraction

The technology is growing and increasing in our day to day life to satisfy the needs of human beings. The system we are going to propose makes the human job easier. Here the YOLO algorithm which is a deep learning object detection architecture is used to detect the number plate of the vehicle. After detecting the number plate it converts the vehicle number to a text format. Then it checks it with the database to see if the vehicle is authorized to enter into the premise or not. This system can be implemented in highly restrained areas such as military areas, government organizations, Parliament, etc. This proposed system has around six stages: Capture Image, Search for black pixels, Image filtering, Plate region extraction, character extraction, OCR for character recognition. The alphanumeric characters are identified using the OCR algorithm. It is then used to compare the obtained result from the YOLO algorithm with the database and then check if the vehicle is allowed to enter the premise or not. This proposed system is simulated and implemented using Python, and it was also tested on real-time images for performance purposes.

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

scholarly journals YORÙBÁNET: A DEEP CONVOLUTIONAL NEURAL NETWORK DESIGN FOR YORÙBÁ ALPHABETS RECOGNITION

2021 ◽  
Vol 5 (11) ◽  
Author(s):  
Oyeniran Oluwashina Akinloye ◽  
Oyebode Ebenezer Olukunle
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Design ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Test Set ◽  
Novel Technique ◽  
Optical Character

Numerous works have been proposed and implemented in computerization of various human languages, nevertheless, miniscule effort have also been made so as to put Yorùbá Handwritten Character on the map of Optical Character Recognition. This study presents a novel technique in the development of Yorùbá alphabets recognition system through the use of deep learning. The developed model was implemented on Matlab R2018a environment using the developed framework where 10,500 samples of dataset were for training and 2100 samples were used for testing. The training of the developed model was conducted using 30 Epoch, at 164 iteration per epoch while the total iteration is 4920 iterations. Also, the training period was estimated to 11296 minutes 41 seconds. The model yielded the network accuracy of 100% while the accuracy of the test set is 97.97%, with F1 score of 0.9800, Precision of 0.9803 and Recall value of 0.9797.

Ambiguity Reduction through Optimal Set of Region Selection Using GA and BFO for Handwritten Bangla Character Recognition

2015 ◽  
pp. 611-639
Author(s):  
Nibaran Das ◽  
Subhadip Basu ◽  
Mahantapas Kundu ◽  
Mita Nasipuri
Keyword(s):  
Character Recognition ◽  
Recognition System ◽  
Human Beings ◽  
Bacterial Foraging Optimization ◽  
Global Features ◽  
Data Set ◽  
Region Selection ◽  
Longest Run ◽  
Ambiguity Reduction ◽  
Optimal Set

To recognize different patterns, identification of local regions where the pattern classes differ significantly is an inherent ability of the human cognitive system. This inherent ability of human beings may be imitated in any pattern recognition system by incorporating the ability of locating the regions that contain the maximum discriminating information among the pattern classes. In this chapter, the concept of Genetic Algorithm (GA) and Bacterial Foraging Optimization (BFO) are discussed to identify those regions having maximum discriminating information. The discussion includes the evaluation of the methods on the sample images of handwritten Bangla digit and Basic character, which is a subset of Bangla character set. Different methods of sub-image or local region creation such as random creation or based on the Center of Gravity (CG) of the foreground pixels are also discussed here. Longest run features, extracted from the generated local regions, are used as local feature in the present chapter. Based on these extracted local features, together with global features, the algorithms are applied to search for the optimal set of local regions. The obtained results are higher than that results obtained without optimization on the same data set.

Ambiguity Reduction Through Optimal Set of Region Selection Using GA and BFO for Handwritten Bangla Character Recognition

2018 ◽  
pp. 1279-1306
Author(s):  
Nibaran Das ◽  
Subhadip Basu ◽  
Mahantapas Kundu ◽  
Mita Nasipuri
Keyword(s):  
Character Recognition ◽  
Recognition System ◽  
Human Beings ◽  
Bacterial Foraging Optimization ◽  
Global Features ◽  
Data Set ◽  
Region Selection ◽  
Longest Run ◽  
Ambiguity Reduction ◽  
Optimal Set

To recognize different patterns, identification of local regions where the pattern classes differ significantly is an inherent ability of the human cognitive system. This inherent ability of human beings may be imitated in any pattern recognition system by incorporating the ability of locating the regions that contain the maximum discriminating information among the pattern classes. In this chapter, the concept of Genetic Algorithm (GA) and Bacterial Foraging Optimization (BFO) are discussed to identify those regions having maximum discriminating information. The discussion includes the evaluation of the methods on the sample images of handwritten Bangla digit and Basic character, which is a subset of Bangla character set. Different methods of sub-image or local region creation such as random creation or based on the Center of Gravity (CG) of the foreground pixels are also discussed here. Longest run features, extracted from the generated local regions, are used as local feature in the present chapter. Based on these extracted local features, together with global features, the algorithms are applied to search for the optimal set of local regions. The obtained results are higher than that results obtained without optimization on the same data set.

scholarly journals Optical Character Recognition using CRNN

2020 ◽  
Vol 9 (8) ◽  
pp. 115-120
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Character Recognition ◽  
Web Application ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Feature Maps ◽  
Sequence Modeling ◽  
Optical Character ◽  
Network Component

Optical Character Recognition (OCR) is a computer vision technique which recognizes text present in any form of images, such as scanned documents and photos. In recent years, OCR has improved significantly in the precise recognition of text from images. Though there are many existing applications, we plan on exploring the domain of deep learning and build an optical character recognition system using deep learning architectures. In the later stage, this OCR system is developed to form a web application which provides the functionalities. The approach applied to achieve this is to implement a hybrid model containing three components namely, the Convolutional Neural Network component, the Recurrent Neural Network component and the Transcription component which decodes the output from RNN into the corresponding label sequence. The process of solving problems involving text recognition required CNN to extract feature maps from images. These sequence of feature vectors undergo sequence modeling through the RNN component predicting label distributions which are later translated using the Connectionist Temporal Classification technique in the transcription layer. The model implemented acts as the backend of the web application developed using the Flask web framework. The complete application is later containerized into an image using Docker. This helps in easy deployment on the application along with its environment across any system.

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