RECOGNITION OF HANDWRITTEN SIMILAR CHINESE CHARACTERS BY SELF-GROWING PROBABILISTIC DECISION-BASED NEURAL NETWORK

1999 ◽  
Vol 09 (06) ◽  
pp. 545-561 ◽  
Author(s):  
HSIN-CHIA FU ◽  
Y. Y. XU ◽  
H. Y. CHANG
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Difficult Problem ◽  
Input Image ◽  
Prototype System ◽  
Credit Assignment ◽  
Similar Character

Recognition of similar (confusion) characters is a difficult problem in optical character recognition (OCR). In this paper, we introduce a neural network solution that is capable of modeling minor differences among similar characters, and is robust to various personal handwriting styles. The Self-growing Probabilistic Decision-based Neural Network (SPDNN) is a probabilistic type neural network, which adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme. Based on the SPDNN model, we have constructed a three-stage recognition system. First, a coarse classifier determines a character to be input to one of the pre-defined subclasses partitioned from a large character set, such as Chinese mixed with alphanumerics. Then a character recognizer determines the input image which best matches the reference character in the subclass. Lastly, the third module is a similar character recognizer, which can further enhance the recognition accuracy among similar or confusing characters. The prototype system has demonstrated a successful application of SPDNN to similar handwritten Chinese recognition for the public database CCL/HCCR1 (5401 characters × 200 samples). Regarding performance, experiments on the CCL/HCCR1 database produced 90.12% recognition accuracy with no rejection, and 94.11% accuracy with 6.7% rejection, respectively. This recognition accuracy represents about 4% improvement on the previously announced performance.5,11 As to processing speed, processing before recognition (including image preprocessing, segmentation, and feature extraction) requires about one second for an A4 size character image, and recognition consumes approximately 0.27 second per character on a Pentium-100 based personal computer, without use of any hardware accelerator or co-processor.

scholarly journals Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network

2021 ◽  
Vol 9 (2) ◽  
pp. 73-84
Author(s):  
Md. Shahadat Hossain ◽  
Md. Anwar Hossain ◽  
AFM Zainul Abadin ◽  
Md. Manik Ahmed
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Network Performance ◽  
Deep Neural Network ◽  
Recognition Accuracy ◽  
Digit Recognition ◽  
Optical Character ◽  
Regularization Parameters ◽  
Handwritten Recognition

The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods.

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.

scholarly journals A Bilingual Numeral OCR System for Creating Uni-Lingual Digitized Numeral Document

2015 ◽  
Vol 9 (13) ◽  
pp. 148 ◽  
Author(s):  
Karthick K ◽  
Chitra S
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Input Image ◽  
Self Organizing Map ◽  
Text Documents ◽  
Library Automation ◽  
Text Document ◽  
Billing System ◽  
Postal Automation

<p>The optical character recognition has been used in many applications such as dictionary generation, customer billing system, banking and postal automation, and library automation etc. The bilingual OCR system to make uni-lingual script helps us to reduce the requirement of two different OCR systems into a single OCR system for recognition of two different languages. This type of globalization helps the universal users of any language can read the text documents in their self-language if the bilingual documents are converted into uni-lingual document. In this paper, the image which contains printed Tamil and European numerals has been recognized using common OCR System and the Tamil numerals are converted into European numerals to globalize the document from a bilingual script into a uni-lingual document. The main objective of the work is to bring out the single numeral (European numerals) text document from the input image with two different numerals (Tamil and European Numerals). The Kohonen’s self-organizing map (SOM) based recognition system has been used for recognizing the numerals and recognized characters in bilingual numerals (Tamil and European Numerals) form are converted into Uni-lingual form (European numerals). This paper also discusses the various approaches used for OCR.</p>

scholarly journals SLOPE AND CURVE CODING TO RECOGNIZE ENGLISH NUMERALS

2020 ◽  
Vol 8 (2) ◽  
pp. 15-20
Author(s):  
Binod Kumar Prasad
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Accuracy ◽  
Recognition System ◽  
The Novel ◽  
Nearest Neighbour ◽  
Global Database ◽  
Optical Character ◽  
Individual Character ◽  
Nearest Neighbour Classifier

Purpose: Lines and Curves are important parts of characters in any script. Features based on lines and curves go a long way to characterize an individual character as well as differentiate similar-looking characters. The present paper proposes an English numerals recognition system using feature elements obtained from the novel and efficient coding of the curves and local slopes. The purpose of this paper is to recognize English numerals efficiently to develop a reliable Optical Character recognition system. Methodology: K-Nearest Neighbour classification technique has been implemented on a global database MNIST to get an overall recognition accuracy rate of 96.7 %, which is competitive to other reported works in literature. Distance features and slope features are extracted from pre-processed images. The feature elements from training images are used to train K-Nearest-Neighbour classifier and those from test images have been used to classify them. Main Findings: The findings of the current paper can be used in Optical Character Recognition (OCR) of alphanumeric characters of any language, automatic reading of amount on bank cheque, address written on envelops, etc. Implications: Due to the similarity in structures of some numerals like 2, 3, and 8, the system produces respectively lower recognition accuracy rates for them. Novelty: The ways of finding distance and slope features to differentiate the curves in the structure of English Numerals is the novelty of this work.

scholarly journals KANNADA CHARACTER RECOGNITION SYSTEM USING NEURAL NETWORK

2012 ◽  
pp. 206-208
Author(s):  
SURESH KUMAR D S ◽  
AJAY KUMAR B R ◽  
K SRINIVASA KALYAN
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Recognition Accuracy ◽  
Handwriting Recognition ◽  
Recognition System ◽  
Indian Language ◽  
Research Areas ◽  
The Neural Network ◽  
Feed Forward Neural Networks ◽  
Hidden Layer

Handwriting recognition has been one of the active and challenging research areas in the field of pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form[1]. As there are no sufficient number of works on Indian language character recognition especially Kannada script among 15 major scripts in India[2].In this paper an attempt is made to recognize handwritten Kannada characters using Feed Forward neural networks. A handwritten kannada character is resized into 20x30 pixel.The resized character is used for training the neural network. Once the training process is completed the same character is given as input to the neural network with different set of neurons in hidden layer and their recognition accuracy rate for different kannada characters has been calculated and compared. The results show that the proposed system yields good recognition accuracy rates comparable to that of other handwritten character recognition systems.

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.

scholarly journals Recognition of Handwritten Sheba Character using Artificial Neural Network

2021 ◽  
Vol 10 (2) ◽  
pp. 1264-1276
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Daily Life ◽  
Recognition System ◽  
Arabic Language ◽  
Study Analysis ◽  
Optical Character ◽  
Computer Based ◽  
Japanese English

From the Last twenty years, the computer-based mechanism has an essential process in daily life and research-oriented applications, the whole world be attracted by computers and approximately all the main processing is being completed automatically. Recognition of handwriting now it is an eye-catching and tough study analysis in image processing and pattern identification field in the today’s world. To beat this problem Optical Character Recognition system (OCR) is practice and concentrated research has been carrying on OCR. Numerous OCR systems are existing in the market other than mainly of this system working for Japanese, English, Chinese, Roman letters. There is no adequate work on Arabic language particularly Sheba characters. In proposed paper, an OCR system for Sheba recognition of character depend on neural network is presented. After analysing various method for segmentation and pre-processing A and B which are used for image pre-processing and segmentation respectively to enhance performance for projected framework. All the issues and challenges for OCR system and relevancy or accuracy of proposed method are discussed and analysed briefly

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