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

Hamdi, Yahia; Boubaker, Houcine; Rabhi, Besma; Ouarda, Wael; Alimi, Adel M.

doi:10.36227/techrxiv.13903661.v3

Cited by 2 publications

(1 citation statement)

References 40 publications

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“…Hamidi et al [68] proposed a multi-stage architecture that uses CNN, Beta-Elliptic Model (BEM) for features extraction, and Deep Bidirectional Long Short Term Memory (DBLSTM) and SVM networks for classification. CNN works on offline data, while BEM extracts visual characteristics of online data.…”

Section: Related Workmentioning

confidence: 99%

A new hybrid model of convolutional neural networks and hidden Markov chains for image classification

Goumiri

Benboudjema

Pieczynski

2023

Neural Comput & Applic

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Convolutional neural networks (CNNs) have lately proven to be extremely effective in image recognition. Besides CNN, hidden Markov chains (HMCs) are probabilistic models widely used in image processing. This paper presents a new hybrid model composed of both CNNs and HMCs. The CNN model is used for feature extraction and dimensionality reduction and the HMC model for classification. In the new model, named CNN-HMC, convolutional and pooling layers of the CNN model are applied to extract features maps. Also a Peano scan is applied to obtain several HMCs. Expectation–Maximization (EM) algorithm is used to estimate HMC’s parameters and to make the Bayesian Maximum Posterior Mode (MPM) classification method used unsupervised. The objective is to enhance the performances of the CNN models for the image classification task. To evaluate the performance of our proposal, it is compared to six models in two series of experiments. In the first series, we consider two CNN-HMC and compare them to two CNNs, 4Conv and Mini AlexNet, respectively. The results show that CNN-HMC model outperforms the classical CNN model, and significantly improves the accuracy of the Mini AlexNet. In the second series, it is compared to four models CNN-SVMs, CNN-LSTMs, CNN-RFs, and CNN-gcForests, which only differ from CNN-HMC by the second classification step. Based on five datasets and four metrics recall, precision, F1-score, and accuracy, results of these comparisons show again the interest of the proposed CNN-HMC. In particular, with a CNN model of 71% of accuracy, the CNN-HMC gives an accuracy ranging between 81.63% and 92.5%.

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Section: Related Workmentioning

confidence: 99%