scholarly journals MyOcrTool: Visualization System for Generating Associative Images of Chinese Characters in Smart Devices

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Laxmisha Rai ◽  
Hong Li
Keyword(s):  
Real Time ◽  
Chinese Text ◽  
Character Recognition ◽  
Recognition System ◽  
Practical Significance ◽  
Smart Devices ◽  
Automatic Identification ◽  
Chinese Characters ◽  
Visualization System ◽  
Writing Chinese

Majority of Chinese characters are pictographic characters with strong associative ability and when a character appears for Chinese readers, they usually associate with the objects, or actions related to the character immediately. Having this background, we propose a system to visualize the simplified Chinese characters, so that developing any skills of either reading or writing Chinese characters is not necessary. Considering the extensive use and application of mobile devices, automatic identification of Chinese characters and display of associative images are made possible in smart devices to facilitate quick overview of a Chinese text. This work is of practical significance considering the research and development of real-time Chinese text recognition, display of associative images and for such users who would like to visualize the text with only images. The proposed Chinese character recognition system and visualization tool is named as MyOcrTool and developed for Android platform. The application recognizes the Chinese characters through OCR engine, and uses the internal voice playback interface to realize the audio functions and display the visual images of Chinese characters in real-time.

scholarly journals Handwritten capital letter recognition based on OpenCV

2019 ◽  
Vol 277 ◽  
pp. 02030 ◽  
Author(s):  
Yuncong Lu
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Letter Recognition ◽  
Practical Significance ◽  
Capital Letter ◽  
High Recognition Accuracy ◽  
The Fourier Transform ◽  
Main Components ◽  
Repeated Training

Handwriting capitalization recognition is a function of distinguishing handwritten capital letters by means of machine or computer intelligence, which is classified into the field of optical character recognition. Given that capital letters are widely used around the world, identification and analysis are often used as the main components of some control systems. Therefore, the research on handwritten capital letter recognition is also very practical and has important practical significance. The key part of the research contained in this paper is the image preprocessing and the optimal selection of feature vectors, and finally completes the design of handwritten digit recognition system. In this paper, the Fourier and Bayesian commonly used are compared, and eventually the Fourier transform feature is applied to the system classification identification. After completing the test on the relevant experimental data, the results show that the handwritten capital recognition system established in this paper has a high recognition accuracy for handwritten capital letters after repeated training.

scholarly journals A Genetic-LVQ neural networks approach for handwritten Arabic character recognition

2018 ◽  
Vol 7 (2) ◽  
pp. 43
Author(s):  
Abir Alharbi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Character Recognition ◽  
Recognition System ◽  
Smart Devices ◽  
Binary Matrix ◽  
Recognition Systems ◽  
Handwritten Recognition ◽  
Handwritten Arabic

Handwritten recognition systems are a dynamic field of research in areas of artificial intelligence. Many smart devices available in the market such as pen-based computers, tablets, mobiles with handwritten recognition technology need to rely on efficient handwritten recognition systems. In this paper we present a novel Arabic character handwritten recognition system based on a hybrid method consisting of a genetic algorithm and a Learning vector quantization (LVQ) neural network. Sixty different handwritten Arabic character datasets are used for training the neural network. Each character dataset contains 28 letters written twice with 15 distinct shaped alphabets, and each handwritten Arabic letter is represented by a binary matrix that is used as an input to a genetic algorithm for feature selection and dimension reduction to include only the most effective features to be fed to the LVQ classifier. The recognition process in the system involves several essential steps such as: handwritten letter acquisition, dataset preparation, feature selection, training, and recognition. Comparing our results to those acquired by the whole feature dataset without selection, and to the results using other classification algorithms confirms the effectiveness of our proposed handwritten recognition system with an accuracy of 95.4%, hence, showing a promising potential for improving future handwritten Arabic recognition devices in the market.

scholarly journals A Real-Time Automatic Plate Recognition System Based on Optical Character Recognition and Wireless Sensor Networks for ITS

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 55
Author(s):  
Nicole do Vale Dalarmelina ◽  
Marcio Andrey Teixeira ◽  
Rodolfo I. Meneguette
Keyword(s):  
Real Time ◽  
Intelligent Transportation Systems ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Transportation Systems ◽  
Identification System ◽  
License Plate ◽  
Optical Character ◽  
Smart Parking

Automatic License Plate Recognition has been a recurrent research topic due to the increasing number of cameras available in cities, where most of them, if not all, are connected to the Internet. The video traffic generated by the cameras can be analyzed to provide useful insights for the transportation segment. This paper presents the development of an intelligent vehicle identification system based on optical character recognition (OCR) method to be used on intelligent transportation systems. The proposed system makes use of an intelligent parking system named Smart Parking Service (SPANS), which is used to manage public or private spaces. Using computer vision techniques, the SPANS system is used to detect if the parking slots are available or not. The proposed system makes use of SPANS framework to capture images of the parking spaces and identifies the license plate number of the vehicles that are moving around the parking as well as parked in the parking slots. The recognition of the license plate is made in real-time, and the performance of the proposed system is evaluated in real-time.

Typeface Identification for Printed Chinese Characters

1998 ◽  
Vol 12 (02) ◽  
pp. 173-190 ◽  
Author(s):  
Yi-Hong Tseng ◽  
Chi-Chang Kuo ◽  
Hsi-Jian Lee
Keyword(s):  
Aspect Ratio ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Chinese Characters ◽  
Statistical Features ◽  
Membership Functions ◽  
Identification Rate ◽  
Stroke Width ◽  
Input Character

In this paper, we propose a methodology for identifying typefaces of printed Chinese characters in documents. Three kinds of features, stroke width means, stroke width variations, and aspect ratio, are first used to classify character typefaces as: Black, Li, Kai-Round, or Ming-Song. Each of the last two groups contains two typefaces. Vertical/horizontal stroke width ratios are used to distinguish between the Ming and Song typefaces and accumulative pixel ratio to distinguish between the Kai and Round typefaces. Six different typeface feature distributions measured from 5401 printed Chinese characters are considered, and a trapezoid-shaped membership function is constructed for each distribution. Based on these membership functions, we determine what typeface each input character belongs to using a two-level decision tree. To increase the identification rate, the typeface of a certain character is adjusted according to the typeface identification results of the front and the next characters. In the character recognition system, we use two statistical features: crossing counts and contour directional counts. We achieved an 89.87% typeface identification rate in our experiments, and a 95.60% character recognition rate.

A real time numeric character recognition system using artificial neural network

2013 ◽  
Author(s):  
M. M. Farhad ◽  
S. M. Nafiul Hossain ◽  
Md. Imtiaz Hossain ◽  
Ripon Shaha Mishu ◽  
Shamim Shahriar Hossain ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Character Recognition ◽  
Recognition System ◽  
Artificial Neural

scholarly journals Pengenalan Karakter Plat Nomor Mobil Secara Real Time

2013 ◽  
Vol 7 (1) ◽  
pp. 35
Author(s):  
Ikhwan Ruslianto ◽  
Agus Harjoko
Keyword(s):  
Success Rate ◽  
Real Time ◽  
Character Recognition ◽  
Template Matching ◽  
Component Analysis ◽  
Recognition System ◽  
Character Segmentation ◽  
Connected Component ◽  
Connected Component Analysis ◽  
Complex Background

AbstrakPengenalan plat nomor di Indonesia biasanya digunakan pada sistem parkir yang masih dilakukan secara manual, yaitu dengan mencatat karakter plat nomor oleh petugas jaga parkir. Padahal pengenalan plat nomor tidak hanya dilakukan untuk system perparkiran tetapi dapat digunakan untuk menemukan kendaraan yang melanggar peraturan lalu lintas dijalan raya secara real time, misalnya pelaku tabrak lari pada kecelakaan maupun kendaraan yang melanggar rambu-rambu lalu lintas.Penelitian ini memberikan alternatif pengenalan karakter plat nomor mobil menggunakan metode connected component analysis dan matching sehingga dapat menyelesaikan permasalahan dengan background yang kompleks dan mobil yang bergerak dijalan raya.Metode connected component analysis berhasil melakukan proses segmentasi plat dan segmentasi karakter dengan kondisi background yang kompleks secara tepat terhadap 67 sampel citra dengan tingkat keberhasilan 95,52% untuk segmentasi plat dan 94,98% untuk segmentasi karakter dan metode template matching berhasil melakukan proses pengenalan karakter secara akurat dengan tingkat keberhasilan 87,45%. Kata kunci— real time, connected component analysis, template matching  Abstract Indonesia’s number plat recognition system are typically used in parking lots that are still done manually, by recording the license plate characters by parking guard. Though number plate recognition system is not only for parking but can be used to find vehicles that violate traffic rules highway street in real time, such as actors on the hit and run accident and the vehicles that violate traffic signs.This study provides an alternative car number plate character recognition using connected component analysis and matching so as to solve problems with complex background and a moving car on the road.Connected component analysis method successfully to the plates segmentation and character segmentation in complex background condition are appropriate to the 67 sample images with the success rate of 95.52% for the plate segmentation and 94.98% for plate character segmentation and template matching method successfully perform the character recognition process accurately with a success rate of 87.45%. Keywords— real time, connected component analysis, template matching

Sign in / Sign up

Export Citation Format

Share Document