A HYBRID SCHEME FOR HANDPRINTED NUMERAL RECOGNITION BASED ON A SELF-ORGANIZING NETWORK AND MLP ClASSIFIERS

2002 ◽  
Vol 16 (07) ◽  
pp. 845-864 ◽  
Author(s):  
UJJWAL BHATTACHARYA ◽  
TANMOY KANTI DAS ◽  
AMITAVA DATTA ◽  
SWAPAN KUMAR PARUI ◽  
BIDYUT BARAN CHAUDHURI
Keyword(s):  
Recognition Rate ◽  
Structural Features ◽  
Data Set ◽  
Novel Approach ◽  
Tree Classifier ◽  
Sample Data ◽  
Validation Set ◽  
Numeral Recognition ◽  
Correct Recognition Rate ◽  
Self Organizing

This paper proposes a novel approach to automatic recognition of handprinted Bangla (an Indian script) numerals. A modified Topology Adaptive Self-Organizing Neural Network is proposed to extract a vector skeleton from a binary numeral image. Simple heuristics are considered to prune artifacts, if any, in such a skeletal shape. Certain topological and structural features like loops, junctions, positions of terminal nodes, etc. are used along with a hierarchical tree classifier to classify handwritten numerals into smaller subgroups. Multilayer perceptron (MLP) networks are then employed to uniquely classify the numerals belonging to each subgroup. The system is trained using a sample data set of 1800 numerals and we have obtained 93.26% correct recognition rate and 1.71% rejection on a separate test set of another 7760 samples. In addition, a validation set consisting of 1440 samples has been used to determine the termination of the training algorithm of the MLP networks. The proposed scheme is sufficiently robust with respect to considerable object noise.

A Self-Organizing Hierarchical Classifier for Multi-Lingual Large-Set Oriental Character Recognition

1998 ◽  
Vol 12 (02) ◽  
pp. 191-208 ◽  
Author(s):  
Hee-Seon Park ◽  
Hee-Heon Song ◽  
Seong-Whan Lee
Keyword(s):  
Character Recognition ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Large Set ◽  
Feature Extraction Method ◽  
Tree Classifier ◽  
Hierarchical Neural Network ◽  
Dot Density ◽  
Hierarchical Classifier ◽  
Self Organizing

In this paper, we propose a practical scheme for multi-lingual, multi-font and multi-size large-set Oriental character recognition using a self-organizing hierarchical neural network classifier. In order to absorb the variation of the character shapes in multi-font and multi-size characters, a modified nonlinear shape normalization method based on dot density was introduced, and also to represent the different topological structures of multi-lingual characters effectively, a hierarchical feature extraction method was adopted. For coarse classification, a tree classifier and SOFM/LVQ based classifier which is composed of an adaptive SOFM coarse-classifier and an LVQ4 language-classifier were considered. For fine classification, a classifier based on LVQ4 learning algorithm has been developed. The experimental results revealed that the proposed scheme has the highest recognition rate of 98.27% for testing data with 7,320 kinds of multi-lingual classes and the time performance of more than 40 characters per second on 486DX-2 66MHz PC.

scholarly journals Detection of Brain Tumor Using K-Nearest Neighbor (KNN) Based Classification Model and Self Organizing Map (SOM) Algorithm

2020 ◽  
Vol 7 ◽  
Keyword(s):  
Data Mining ◽  
Brain Tumor ◽  
Nearest Neighbor ◽  
Classification Model ◽  
Discrete Wavelet ◽  
Self Organizing Map ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Novel Approach ◽  
Self Organizing

Knowledge discovery is also known as Data mining in databases, in recent years that technique plays a major role in research area. Data mining in healthcare domain has noteworthy usage in real world. The mining method can enable the healthcare field for the enhancement of institutionalization of its administrations and become quicker with best in class technologies. Innovation utilization isn't restricted to basic leadership in undertakings, yet spread to different social statuses in all fields. In this paper a novel approach for the detection of brain tumor is proposed. The novel approach uses the classification technique of K-nearest neighbor (KNN) and for ignoring the error of the dataset image SOM (self-organizing map) algorithm has been used. Discrete wavelet transform (DWT) is used for transforming input image data set, in which RGB color of input data image has been converted into gray scale. Then it has been classified using KNN after that the error avoiding algorithm has been carried out. This will help to differentiate tumor cells and the normal cells. The presence of tumor in brain image is detected using parametric analysis by simulation.

Multi-Bank Check Recognition System: Consideration on the Numeral Amount Recognition Module

1997 ◽  
Vol 11 (04) ◽  
pp. 595-618 ◽  
Author(s):  
L. Heutte ◽  
P. Barbosa-Pereira ◽  
O. Bougeois ◽  
J. V. Moreau ◽  
B. Plessis ◽  
...  
Keyword(s):  
Contextual Information ◽  
Recognition Rate ◽  
Recognition System ◽  
Structural Features ◽  
Contextual Analysis ◽  
Syntactic Analysis ◽  
Good Representation ◽  
Automatic Reading ◽  
Reading System ◽  
Numeral Recognition

This paper presents a complete numeral amount recognition module which is integrated in an automatic system aimed at reading all types of French checks. This module is combined with an automatic reading system of literal amounts. This complete working system, called LIREChèques, is developed by MATRA MS&I and is now in advanced test at SERINTEL, a pilot site. Two aspects of the numeral amount recognition system are particularly emphasized: the numeral recognition stage itself and the syntactic analysis stage. The numeral recognition module relies on a combination of two individual classifiers, the first one is based on concavity measurements, the second one on both statistical and structural features. The syntactic analysis, called syntactic/contextual analysis, is combined with contextual information to take into account the segmentation behaviour and the presence of literal entities in the numeral amount. We demonstrate that very good performances can be obtained on digits such as those extracted from numeral amounts since a substitution rate of 0.06% while still preserving a recognition rate of near 87% can be achieved. As for the syntactic/contextual analysis stage, results obtained on a test set (containing checks from more than 40 different banks and 15/ of typed checks, thus being a good representation of the real tests realized on site) show clearly that introduction of contextual information in association with syntactic analysis allows to process much more numeral amounts than a simple syntactic analysis and increases perceptibility of the recognition rate.

Abstract 14131: Proof of Concept: Interpretation of EKG With Image Recognition and Convolutional Neural Networks

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Subrat Das ◽  
Matthew Epland ◽  
Jiang Yu ◽  
Ranjit Suri
Keyword(s):  
Image Recognition ◽  
Gradient Descent ◽  
Confusion Matrix ◽  
Stochastic Gradient Descent ◽  
Training Set ◽  
Data Set ◽  
Computing Power ◽  
The Past ◽  
Novel Approach ◽  
Validation Set

Introduction: EKGs are the cornerstone of management in cardiovascular diseases. There have been multiple efforts to computerize the EKG interpretation with algorithms, which unfortunately are machine specific and proprietary. We propose the development of an image recognition model which can be used to read EKG strips (which use standard notations) and hence be used universally. Method: A convolutional neural network (CNN) was trained to classify 12-lead EKGs between seven clinically important diagnostic classes (Figure 1a). Pre-labeled EKG recordings (6-60s) from a publicly available data set on PhysioNet were used to construct the images. The EKG images displayed the 12 channel traces, of 2.5s each, on a consistent 4x3 grid at a resolution of 800x800 pixels (Figure 1a). The data set (23,336 images) was divided into training, tuning, and validation sets; containing 70%, 15%, and 15% of the images, respectively. An austere variation of the MobileNetV3 model was trained from the ground up on the labeled training set. Stochastic gradient descent (SGD) was used to minimize the cross-entropy loss. Training was halted when the tuning loss had not improved from its previous minimum by 0.05% over the past 10 epochs. Results: The model trained over 52 epochs of batches of 32 images. The model’s accuracy was tested using the validation set (which was not used for development of model) and reported as a confusion matrix (Figure 1b). The accuracy per class varies from 69-91%. Conclusion: We used a labeled dataset of EKG images to develop a CNN model to predict seven different diagnostic classes with good accuracy. This is a novel approach to EKG interpretation as an image recognition problem and thus generates the ability to create diagnostic algorithms that are not dependent on proprietary voltage signals generated by commercial EKG machines. With the addition of more images to the data set and higher computing power we are confident that we can achieve enhanced accuracy.

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

Grand Canyon riverbed sediment changes, experimental release of September 2000 - a sample data set

2003 ◽  
Author(s):  
Florence L. Wong ◽  
Roberto J. Anima ◽  
Peter Galanis ◽  
Jennifer Codianne ◽  
Yu Xia ◽  
...  
Keyword(s):  
Grand Canyon ◽  
Data Set ◽  
Sample Data ◽  
Riverbed Sediment ◽  
Experimental Release

scholarly journals Application of portrait recognition system for emergency evacuation in mass emergencies

2021 ◽  
Vol 30 (1) ◽  
pp. 893-902
Author(s):  
Ke Xu
Keyword(s):  
Detection Rate ◽  
Characteristic Curve ◽  
Recognition Rate ◽  
Recognition System ◽  
Emergency Evacuation ◽  
Shopping Mall ◽  
Single Shot ◽  
Data Set ◽  
Linear Discriminant ◽  
Adaboost Algorithm

Abstract A portrait recognition system can play an important role in emergency evacuation in mass emergencies. This paper designed a portrait recognition system, analyzed the overall structure of the system and the method of image preprocessing, and used the Single Shot MultiBox Detector (SSD) algorithm for portrait detection. It also designed an improved algorithm combining principal component analysis (PCA) with linear discriminant analysis (LDA) for portrait recognition and tested the system by applying it in a shopping mall to collect and monitor the portrait and establish a data set. The results showed that the missing detection rate and false detection rate of the SSD algorithm were 0.78 and 2.89%, respectively, which were lower than those of the AdaBoost algorithm. Comparisons with PCA, LDA, and PCA + LDA algorithms demonstrated that the recognition rate of the improved PCA + LDA algorithm was the highest, which was 95.8%, the area under the receiver operating characteristic curve was the largest, and the recognition time was the shortest, which was 465 ms. The experimental results show that the improved PCA + LDA algorithm is reliable in portrait recognition and can be used for emergency evacuation in mass emergencies.

Online Biological Literature MRI Image Classification Study

2013 ◽  
Vol 694-697 ◽  
pp. 2336-2340
Author(s):  
Yun Feng Yang ◽  
Feng Xian Tang
Keyword(s):  
Recognition Performance ◽  
Recognition Rate ◽  
Image Features ◽  
Feature Identification ◽  
Identification Method ◽  
Image Library ◽  
Text Information ◽  
Correct Recognition Rate ◽  
Biological Literature ◽  
Mri Image

In order to construct a certain standard structure MRI (Magnetic resonance imaging) image library by extracting and collating unstructured literature data information, an identification method of the image and text information fusion is proposed. The method makes use of PHOW (Pyramid Histogram Of Words) to represent image features, combines with the word frequency characteristics of the embedded icon note (text), and then uses posterior multiplication fusion method to complete the classification and identification of the online biological literature MRI image. The experimental results show that this method has better correct recognition rate and better recognition performance than feature identification method only based on PHOW or text. The study can offer use for reference to construct other structured professional database from online literature.

scholarly journals Pedestrian detection algorithm in traffic scene based on weakly supervised hierarchical deep model

2016 ◽  
Vol 14 (1) ◽  
pp. 172988141769231 ◽  
Author(s):  
Yingfeng Cai ◽  
Youguo He ◽  
Hai Wang ◽  
Xiaoqiang Sun ◽  
Long Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pedestrian Detection ◽  
Recognition Rate ◽  
Detection Algorithm ◽  
Training Methods ◽  
Two Dimensional ◽  
Data Set ◽  
Deep Model ◽  
Unsupervised Training ◽  
Weakly Supervised

The emergence and development of deep learning theory in machine learning field provide new method for visual-based pedestrian recognition technology. To achieve better performance in this application, an improved weakly supervised hierarchical deep learning pedestrian recognition algorithm with two-dimensional deep belief networks is proposed. The improvements are made by taking into consideration the weaknesses of structure and training methods of existing classifiers. First, traditional one-dimensional deep belief network is expanded to two-dimensional that allows image matrix to be loaded directly to preserve more information of a sample space. Then, a determination regularization term with small weight is added to the traditional unsupervised training objective function. By this modification, original unsupervised training is transformed to weakly supervised training. Subsequently, that gives the extracted features discrimination ability. Multiple sets of comparative experiments show that the performance of the proposed algorithm is better than other deep learning algorithms in recognition rate and outperforms most of the existing state-of-the-art methods in non-occlusion pedestrian data set while performs fair in weakly and heavily occlusion data set.

Association ofEMCNwith Susceptibility to Rheumatoid Arthritis in a Japanese Population

2010 ◽  
Vol 38 (2) ◽  
pp. 221-228 ◽  
Author(s):  
KAZUMASA NISHIMOTO ◽  
KATSUNORI IKARI ◽  
HIROTAKA KANEKO ◽  
SO TSUKAHARA ◽  
YUTA KOCHI ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Association Studies ◽  
Functional Study ◽  
Positive Staining ◽  
Nucleotide Polymorphisms ◽  
Data Set ◽  
Nuclear Factors ◽  
Validation Set ◽  
Synovial Tissues ◽  
Stratified Analysis

Objective.Endomucin, an endothelial-specific sialomucin, is thought to facilitate “lymphocyte homing” to synovial tissues, resulting in the major histopathologies of rheumatoid arthritis (RA). We examined the association between RA susceptibility and the gene coding endomucin,EMCN.Methods.Association studies were conducted with 2 DNA sample sets (initial set of 1504 patients, 752 controls; and validation set, 1113 patients, 940 controls) using 6 tag single-nucleotide polymorphisms (SNP) from the Japanese HapMap database. Immunohistochemistry for the expression of endomucin was conducted with synovial tissues from 4 patients with RA during total knee arthroplasty. Electromobility shift assays were performed for the functional study of identified polymorphisms.Results.Within the initial sample set, the strongest evidence of an association with RA susceptibility was SNP rs3775369 (OR 1.20, p = 0.0075). While the subsequent replication study did not initially confirm the observed significant association (OR 1.13, p = 0.062), an in-depth stratified analysis revealed significant association in patients testing positive to anti-cyclic citrullinated peptide (anti-CCP) antibody in the replication data set (OR 1.15, p = 0.044). Investigating 2 sample sets, significant associations were detected in overall and stratified samples with anti-CCP antibody status (OR 1.17, p = 0.0015). Positive staining for endomucin was detected in all patients. The allele associated with RA susceptibility had a higher binding affinity for HEK298-derived nuclear factors compared to the nonsusceptible allelic variant of rs3775369.Conclusion.A significant association betweenEMCNand RA susceptibility was detected in our Japanese study population. TheEMCNallele conferring RA susceptibility may also contribute to the pathogenesis of RA.

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