AN OFF-LINE TEXT-INDEPENDENT PERSIAN WRITER IDENTIFICATION METHOD

2011 ◽  
Vol 20 (03) ◽  
pp. 489-509 ◽  
Author(s):  
BEHZAD HELLI ◽  
MOHSEN EBRAHIMI MOGHADDAM
Keyword(s):  
Neural Network ◽  
Gabor Filter ◽  
Research Topic ◽  
Experimental Results ◽  
Multi Layer Perceptron ◽  
Writer Identification ◽  
Handwriting Analysis ◽  
Handwritten Documents ◽  
Verification Methods ◽  
Evaluation Phase

The behavioral-biometrics methods of writer identification and verification have been considered as a research topic for many years. However, many writer identification and verification methods have been designed based on English handwriting properties, but because of many differences between English and Persian handwriting and the challenges facing Persian handwriting analysis, designing such methods has many interests in Persian yet. In this paper, we have presented a fully text-independent and texture based method for identifying writers of Persian handwritten documents. As a result of special properties of Persian handwriting, a modified version of Gabor filter that is called Extended Gabor (XGabor) filter has been used to extract the features. An MLP (Multi Layer Perceptron (Node)) neural network and a K-NN classifier have been employed to classify the extracted features. In the evaluation phase, an exhaustive database of Persian handwritten documents was prepared and the method applied on. The experimental results showed that the accuracy of proposed method is about 97% and it is competitive with others. We believe that the proposed method may be extended to identify writers in other languages by adjusting some parameters.

scholarly journals Writer Identification with Hybrid Model using Hybrid HMM and ANN

2019 ◽  
Vol 8 (3) ◽  
pp. 1656-1661
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Hybrid Model ◽  
Optimum Number ◽  
Multi Layer Perceptron ◽  
Writer Identification ◽  
Mlp Model

In this paper, writer identification is performed with three models, namely, HMMBW, HMMMLP and HMMCNN. The features are extracted from the HMM and are classified using Baum Welch algorithm (BW), Multi layer perceptron (MLP) model and Convolutional neural network (CNN) model. A dataset, namely, VTU-WRITER dataset is created for the experiential purpose and the performance of the models were tested. The test train ratio was varied to derive its relation to accuracy. Also the number of states was varied to determine the optimum number of states to be considered in the HMM model. Finally the performance of all the three models is compared

Content Independent Writer Identification on Bangla Script: A Document Level Approach

2018 ◽  
Vol 32 (09) ◽  
pp. 1856011 ◽  
Author(s):  
Chayan Halder ◽  
Sk. Md. Obaidullah ◽  
K. C. Santosh ◽  
Kaushik Roy
Keyword(s):  
Decision Making ◽  
State Of The Art ◽  
Variable Number ◽  
The State ◽  
Experimental Results ◽  
Multi Layer Perceptron ◽  
Writer Identification ◽  
Fields Of Study ◽  
Simple Logistic ◽  
Document Level

Offline writer identification is one of the major fields of study in behavioral biometric. It is a process of matching a questioned document with other documents of known writers to find the appropriate writer. In this paper, local handwriting-based attributes are used as features, and multi-layer perceptron and simple logistic classifiers are used for decision making. The method is tested on an unconstrained handwritten Bangla database of 1383 documents with variable number of datasets from 190 writers. Experimental results show the effectiveness of our system, since it outperforms the state-of-the-art methods by approximately 3% (top-3 and top-4 choices). Further, our method is approximately 27 times faster than conventional segmentation-based methods.

Face Recognition Using Laplacianfaces and Artificial Neural Network

2015 ◽  
Vol 734 ◽  
pp. 633-636
Author(s):  
J.F. Miazonzama ◽  
Qiang Hua ◽  
Liang Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Face Recognition ◽  
Efficient Algorithm ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Research Topic ◽  
Experimental Results ◽  
Locality Preserving

Face recognition has recently become a hot research topic. In order to do more in that area, several algorithms have emerged. However, even the most efficient algorithm has limitations. To overcome this problem, the combination of algorithms is sometimes used. In this paper a methodology based on two approaches is presented. Firstly, we use Locality Preserving Projection (LPP) for feature extraction. Secondly, the Back Propagation Neural Network (BPNN) is used for recognition. Experiments have been done using 400 images of ORL database. Experimental results show that the algorithm is performs well and achieves good recognition.

Gabor filter approximation through a Neural Network applied to fingerprint images

2016 ◽  
Author(s):  
Airam Carlos Pais Barreto Marques ◽  
Antonio Carlos Gay Thomé
Keyword(s):  
Neural Network ◽  
Gabor Filter ◽  
Filter Approximation

scholarly journals DV-DVFS: merging data variety and DVFS technique to manage the energy consumption of big data processing

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hossein Ahmadvand ◽  
Fouzhan Foroutan ◽  
Mahmood Fathy
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Processing Time ◽  
Experimental Results ◽  
The Other ◽  
Data Sets ◽  
Multiple Sources ◽  
Evaluation Phase ◽  
Dynamic Voltage ◽  
Processing Resources

AbstractData variety is one of the most important features of Big Data. Data variety is the result of aggregating data from multiple sources and uneven distribution of data. This feature of Big Data causes high variation in the consumption of processing resources such as CPU consumption. This issue has been overlooked in previous works. To overcome the mentioned problem, in the present work, we used Dynamic Voltage and Frequency Scaling (DVFS) to reduce the energy consumption of computation. To this goal, we consider two types of deadlines as our constraint. Before applying the DVFS technique to computer nodes, we estimate the processing time and the frequency needed to meet the deadline. In the evaluation phase, we have used a set of data sets and applications. The experimental results show that our proposed approach surpasses the other scenarios in processing real datasets. Based on the experimental results in this paper, DV-DVFS can achieve up to 15% improvement in energy consumption.

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