Multi-language online handwriting recognition based on beta-elliptic model and hybrid TDNN-SVM classifier

Zouari, Ramzi; Boubaker, Houcine; Kherallah, Monji

doi:10.1007/s11042-018-6764-0

Cited by 13 publications

(7 citation statements)

References 37 publications

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“…The obtained remains valid even though it is compared to hybridized classifier (98.53% with SVM vs. 96.45% with MLP-HMM, 90% with BCP-LSTM). However, we inferred that our system lost its first place against two recent systems [13,47] with a recognition rate around 100% for both. This can be accounted for in terms of using multi-language data set (contained scripts not only from ADAB data sets) as well as different sets on training and testing(different sets from ADAB) in Refs.…”

Section: • Comparison With State Of Art Workmentioning

confidence: 77%

“…From the obtained results, we can infer how promising our system performance is compared to the state of-the-art systems. First of all, ten different [42] Geometric parameters + Fourier descriptors HMM 85.37 Khlif [43] Online + offline feature HMM 93.33 Hamdi [44] Beta-elliptic model MLP 95.14 Elleuch [45] Online + offline feature SVM 91.8 Abdelaziz [46] Geometric features HMM 97.13 Akouadi [28] Beta parameters + perceptual codes BCP + LSTM 90 Zouari [47] Beta-elliptic parameters TDNN-SVM 100 Maalej [13] x classifier/feature combinations were tried on different sets of ADAB data set. The comparison reveals that our method has much better performance than some approaches relying on a single classifier (98.53% with SVM-based classifier(our approach) vs. 95.14% with MLP and 91.8% with SVM).…”

Section: • Comparison With State Of Art Workmentioning

confidence: 99%

“…This can be accounted for in terms of using multi-language data set (contained scripts not only from ADAB data sets) as well as different sets on training and testing(different sets from ADAB) in Refs. [13] and [47] respectively which prevents performing a thorough comparison. Furthermore, Table 8 discloses a comparative study of the performance of our method on MAYASTROUN data set.…”

Section: • Comparison With State Of Art Workmentioning

confidence: 99%

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Online Handwritten Arabic Scripts Recognition Using Stroke-Based Class Labeling Scheme

Zitouni¹,

Bezine²,

Arous³

2020

IJCIS

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With the increasing availability of pen-based user interfaces, we often come upon multiple data sets of online handwritten scripts such as letters, words, etc., that are collected based on a viable interface. In this paper, we set forward a new method for online handwritten Arabic scripts recognition. Departing from the assumption that handwritten scripts are encoded as a set of strokes, the proposed approach relies first upon classifying strokes contained on the script and then recognizes the whole script. For stroke classification, an support vector machine (SVM) is trained with stroke features vectors obtained from the Beta-elliptic model and fuzzy elementary perceptual codes to obtain class stroke probabilities. The output of this SVM is combined with spatial relation vectors feeding to a second SVM to provide scripts level recognition. The proposed model has been tested on MAYASTROUN dataset. In order to obtain additional insight into the efficiency of the proposed approach, we performed further experiments on ADAB data set. The experimental results highlight its relevance by comfortably outperforming state-of-art systems.

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Section: • Comparison With State Of Art Workmentioning

confidence: 77%

Section: • Comparison With State Of Art Workmentioning

confidence: 99%

Section: • Comparison With State Of Art Workmentioning

confidence: 99%

See 1 more Smart Citation

Online Handwritten Arabic Scripts Recognition Using Stroke-Based Class Labeling Scheme

Zitouni¹,

Bezine²,

Arous³

2020

IJCIS

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“…Further, several researches focus specifically on mono-language script, for instance, Arabic [12], Chinese [7], Tamil [31], Japanese [26] etc. Similarly, there exist multi-script and multi-language systems for online handwriting recognition like those presented in [32], [33] as well as other commercial systems such as those developed by Apple [34] and Microsoft [29] organizations.…”

Section: Deep Learning-based Approachesmentioning

confidence: 99%

Hybrid Architecture based on RNN-SVM for Multilingual Online Handwriting Recognition using Beta-elliptic and CNN models

Hamdi¹,

Boubaker²,

Rabhi³

et al. 2021

Preprint

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Currently, deep learning approaches have proven successful in the areas of handwriting recognition. Despite this, research in this field is still needed, especially in the context of multilingual online handwriting recognition scripts by adopting new network architectures and combining relevant parametric models. In this paper, we propose a multi-stage deep learning-based algorithm for multilingual online handwriting recognition based on hybrid deep Bidirectional Long Short Term Memory (DBLSTM) and SVM networks. The main contributions of our work lie in partly in the composition of a new multi-stage architecture of deep learning networks associated with effective feature vectors that integrate dynamic and visual characteristics. First, the proposed system proceeds by pretreating the acquired script and delimiting its Segments of Online Handwriting Trajectories (SOHTs). Second, two types of feature vectors combining BetaElliptic Model (BEM) and Convolutional Neural Network (CNN) are extracted for each SOHT in order to fuzzy classify them into k sub-groups using DBLSTM neural networks for both online and offline branches trained using an unsupervised fuzzy k-means algorithm. Finally, we combine the trained models to strengthen the discrimination power of the global system using SVM engine. Extensive experiments on three data sets were conducted to validate the performance of the proposed method. The experimental results show the effectiveness and complementarities of the individual modules and the advantage of their fusion.<br>

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“…SVM is considered a powerful tool for linear and nonlinear classification based on a supervised learning algorithm. It has shown high success in many practical applications such as pattern recognition [36]. Contrary to traditionally artificial neural networks, the basic formulation of SVM is the structural risk minimization instead of empirical risk.…”

Section: Svm Enginementioning

confidence: 99%

Handwriting Quality Analysis using Online-Offline Models

Hamdi¹,

Akouaydi²,

Boubaker³

et al. 2021

Preprint

Self Cite

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This work is part of an innovative e-learning project allowing the development of an advanced digital educational tool that provides feedback during the process of learning handwriting for young school children (three to eight years old). In this paper, we describe a new method for children handwriting quality analysis. It automatically detects mistakes, gives real-time on-line feedback for children’s writing, and helps teachers comprehend and evaluate children’s writing skills. The proposed method adjudges five main criteria: shape, direction, stroke order, position respect to the reference lines, and kinematics of the trace. It analyzes the handwriting quality and automatically gives feedback based on the combination of three extracted models: Beta-Elliptic Model (BEM) using similarity detection (SD) and dissimilarity distance (DD) measure, Fourier<br>Descriptor Model (FDM), and perceptive Convolutional Neural Network (CNN) with Support Vector Machine (SVM) comparison engine. The originality of our work lies partly in the system architecture which apprehends complementary dynamic, geometric, and visual representation of the examined handwritten scripts and in the efficient selected features<br>adapted to various handwriting styles and multiple script languages such as Arabic, Latin, digits, and symbol drawing. The application offers two interactive interfaces respectively dedicated to learners, educators, experts or teachers and allows them to adapt it easily to the specificity of their disciples. The evaluation of our framework is enhanced by a database collected in Tunisia primary school with 400 children. Experimental results show the efficiency and robustness of our suggested framework that helps teachers and children by offering positive feedback throughout the handwriting learning process using tactile digital devices.<br>

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Multi-language online handwriting recognition based on beta-elliptic model and hybrid TDNN-SVM classifier

Cited by 13 publications

References 37 publications

Online Handwritten Arabic Scripts Recognition Using Stroke-Based Class Labeling Scheme

Online Handwritten Arabic Scripts Recognition Using Stroke-Based Class Labeling Scheme

Hybrid Architecture based on RNN-SVM for Multilingual Online Handwriting Recognition using Beta-elliptic and CNN models

Handwriting Quality Analysis using Online-Offline Models

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