Isolated handwriting recognition via multi-stage Support Vector Machines

Hajj, Nadine; Awad, Mariette

doi:10.1109/is.2012.6335129

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Because isolated-letter HWR is an essential step for online HWR, we present here a case study on developing an efficient writer-independent HWR system for isolated letters, using pen trajectory modeling for feature extraction and an MSVM for classification (Hajj and Awad 2012). In addition to underlining the importance of the application, this case study illustrates how stationary features are created from sequential data and how a multiclass task is converted into a hierarchical one.…”

Section: Case Study Of Svm For Handwriting Recognitionmentioning

confidence: 99%

Support Vector Machines for Classification

Awad¹,

Khanna²

2015

Efficient Learning Machines

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347

148

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Science is the systematic classification of experience. -George Henry LewesThis chapter covers details of the support vector machine (SVM) technique, a sparse kernel decision machine that avoids computing posterior probabilities when building its learning model. SVM offers a principled approach to machine learning problems because of its mathematical foundation in statistical learning theory. SVM constructs its solution in terms of a subset of the training input. SVM has been extensively used for classification, regression, novelty detection tasks, and feature reduction. This chapter focuses on SVM for supervised classification tasks only, providing SVM formulations for when the input space is linearly separable or linearly nonseparable and when the data are unbalanced, along with examples. The chapter also presents recent improvements to and extensions of the original SVM formulation. A case study concludes the chapter.

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Section: Case Study Of Svm For Handwriting Recognitionmentioning

confidence: 99%

Support Vector Machines for Classification

Awad¹,

Khanna²

2015

Efficient Learning Machines

Self Cite

347

148

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show abstract

“…They did not consider zoning in their work as the number of classes considered by them was limited. Hajj and Awad [30] presented an efficient handwriting recognition system for writer independent isolated letter recognition for English. They modelled pen trajectories for feature extraction and applied multi-stage support vector machines (SVMs) for classification of strokes based on zone.…”

Section: Related Workmentioning

confidence: 99%

Recognition of online handwritten Gurmukhi characters based on zone and stroke identification

Verma

Sharma

2017

Sādhanā

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Handwriting recognition is a technique that converts handwritten characters into a machine-processable format. Handwritten characters can either be presented to machine online or offline. A good amount of research in this area has been carried out for English, Chinese, Japanese and Korean languages. Research is also going on for Indian languages on developing online handwriting recognition systems. Headline and baseline are common features in most Indic languages which divide a character into three zones, namely, upper, middle and lower zones. Identification of headline and baseline is a major task for classification of strokes located in these three zones. A zone identification algorithm is proposed and tested in this text for online handwriting recognition of Gurmukhi script. The strokes are grouped into these separate zones and are recognized based on respective support vector machine model for each zone. A rule-based approach has also been applied and tested for generation of characters from the set of recognized strokes. In this work, an accuracy of 95.3% has been achieved for zone identification and an accuracy of 74.8% has been achieved for character identification for Gurmukhi script. This accuracy has been achieved when the recognition engines of three zones were tested on the dataset of 428 characters each written by 10 users.

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“…Support vector machines (SVMs) [1] have found applications in different fields such as image retrieval [2], handwriting recognition [3] and text classification [4]. In the case of imbalanced data, in which the number of negative patterns, easier to identify and classify, significantly exceeds the positive patterns, which are more difficult to identify and classify, the performance of SVM drops considerably.…”

Section: Introductionmentioning

confidence: 99%

KGEARSRG: Kernel Graph Embedding on Attributed Relational SIFT-Based Regions Graph

Manzo

2019

MAKE

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In real world applications, binary classification is often affected by imbalanced classes. In this paper, a new methodology to solve the class imbalance problem that occurs in image classification is proposed. A digital image is described through a novel vector-based representation called Kernel Graph Embedding on Attributed Relational Scale-Invariant Feature Transform-based Regions Graph (KGEARSRG). A classification stage using a procedure based on support vector machines (SVMs) is organized. Methodology is evaluated through a series of experiments performed on art painting dataset images, affected by varying imbalance percentages. Experimental results show that the proposed approach consistently outperforms the competitors.

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Isolated handwriting recognition via multi-stage Support Vector Machines

Cited by 4 publications

References 14 publications

Support Vector Machines for Classification

Support Vector Machines for Classification

Recognition of online handwritten Gurmukhi characters based on zone and stroke identification

KGEARSRG: Kernel Graph Embedding on Attributed Relational SIFT-Based Regions Graph

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