Multiple classifier decision combination strategies for character recognition: A review

Rahman, Fuad; Fairhurst, Michael

doi:10.1007/s10032-002-0090-8

Cited by 84 publications

(30 citation statements)

References 215 publications

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“…SVM classification method has extraordinary potential capacity. Using kernel function, SVM can well solve the non-linear classification problem [6,20]. SVM discriminates two classes by fitting an optimal linear separating hyperplane (OSH).…”

Section: ) Classifiermentioning

confidence: 99%

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A hierarchical propelled fusion strategy for SAR automatic target recognition

Cui

Cao

Yang

et al. 2013

J Wireless Com Network

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Synthetic aperture radar (SAR) automatic target recognition (ATR) is playing a very important role in military and civil field. Much work has been done to improve the performance of SAR ATR systems. It is well-known that ensemble methods can be used for improving prediction performance. Thus recognition using multiple classifiers fusion (MCF) has become a research hotspot in SAR ATR. Most current researchers focus on the fusion methods by parallel structure. However, such parallel structure has some disadvantages, such as large time consumption, features attribution conflict and low capability on confuser recognition. A hierarchical propelled strategy for multi-classifier fusion (HPSMCF) is proposed in this paper. The proposed HPSMCF has the characters both of series and parallel structure. Features can be used more effective and the recognition efficiency can be improved by extracting features and fusing the probabilistic outputs in a hierarchical propelled way. Meanwhile, the confuser recognition can be achieved by setting thresholds for the confidence in each level. Experiments on MSTAR public data demonstrate that the proposed HPSMCF is robust for variant recognition conditions. Compared with the parallel structure, HPSMCF has better performance both on time consumption and recognition rate.

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Section: ) Classifiermentioning

confidence: 99%

“…These base-level classifiers are respectively constructed using different techniques and methods. With the development of MCF technology, MCF has been extensively applied in many areas, such as character recognition [6], multi-sensor data classification [7] and SAR ATR [8,9].…”

Section: Introductionmentioning

confidence: 99%

A hierarchical propelled fusion strategy for SAR automatic target recognition

Cui

Cao

Yang

et al. 2013

J Wireless Com Network

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“…Combining classifiers to achieve higher accuracy is an important research topic [10,11,12,3,28]. Essentially, the idea behind combining classifiers is based on the so-called divide and conquers principle, according to which a complex computational task is solved by dividing it into a number of computationally simple tasks and then combining the solutions to those tasks [14].…”

Section: Introductionmentioning

confidence: 99%

Off-line Recognition of Persian Handwritten Digits using Statistical Concepts

Rashnoodi¹,

Rashnoodi²,

Rashnoodi³

2012

IJCA

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In this paper a novel method for recognition of Persian handwritten digits is proposed. This approach uses central moments, Covariance, Median and Variance that are obtained at each the box digit image as the features set. The features set consist of 140 dimensions for each instance. In the classification phase of our proposed method the support vector machines, K-Nearest Neighbor and Sequential Minimal Optimization separately are employed. In this paper is used the principal components analysis (PCA) to reduce dimension features set. the performance of these three classifiers is observed on this application in terms of the correct classification and misclassification and how the performance of K-Nearest Neighbor classifier can be improved by varying the value of k.To evaluate our proposed scheme a database of Persian handwritten digits consist of 1699 handwritten digit images is used. In the best case proposed scheme using SMO classifiers yields a recognition rate of 92.3875% for handwritten Farsi numerals.

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“…Finally, the multiple classifier combination is introduced for increasing the accuracy maintained by a single classifier [13]. Horizontal combination of classifiers is mainly used for high accuracy purposes, whereas the cascaded combination is used for speeding up the large set classification.…”

Section: Literature Riviewmentioning

confidence: 99%

Text Recognition using Image Segmentation and Neural Network

Radhi¹

2017

IJCA

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The fast development in worldwide technology makes it essential for us to find solutions for some challenging problems; one of these is the recognition of the important details from images. Text recognition is a process that converts the text that any image contains to something that is recognizable for our modern devices e.g., our smart phones. In this paper an efficient algorithm for the detection of text from an image is proposed. This methodology includes the application of a powerful segmentation method that labels the letters in the image very efficiently, and then the mean of the coordinates of each letter within the image is used after the application of the segmentation method in order to sort the letters correctly, moreover, the algorithm includes the introduction of a new line detection method based on the mean of the labels, and finally some modifications on the work of the autoencoder neural network is proposed in this paper. The transformation of the text in the image to text document is achieved with high accurate result; the results show a complete recognition of the text in wide varieties of images. Examples of the application of this algorithm are for the detection of the text in image with many lines, road sign images and car plate number recognition are shown in this paper.

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Multiple classifier decision combination strategies for character recognition: A review

Cited by 84 publications

References 215 publications

A hierarchical propelled fusion strategy for SAR automatic target recognition

A hierarchical propelled fusion strategy for SAR automatic target recognition

Off-line Recognition of Persian Handwritten Digits using Statistical Concepts

Text Recognition using Image Segmentation and Neural Network

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