RETRACTED ARTICLE: Effective offline handwritten text recognition model based on a sequence-to-sequence approach with CNN–RNN networks

Geetha, R.; Thilagam, T.; Padmavathy, T. V.

doi:10.1007/s00521-020-05556-5

Cited by 32 publications

(17 citation statements)

References 17 publications

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“…We select the same type of text recognition algorithm as a comparison. Recurrent neural network (RNN) [34] is one of the most commonly used algorithms in the field of text recognition. Based on RNN, some researchers have improved the neural network structure and proposed the long short-term memory (LSTM) unit network [35].…”

Section: Resultsmentioning

confidence: 99%

Hybrid Japanese Language Teaching Aid System with Multi-Source Information Fusion Mapping

Zhang

2022

Mathematical Problems in Engineering

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Learning Japanese can enhance competitiveness in a globalized economy, and we address the problems of poor open-source Japanese language teaching, cumbersome teaching tasks, and a single teaching model. We propose a hybrid Japanese teaching aid system with multiple information fusion mapping, which can effectively improve the efficiency of Japanese teaching and reduce the tedious human teaching procedures. The system is divided into two branches of Japanese language recognition, namely, the Japanese text recognition branch and the Japanese voice sequence recognition branch. In the Japanese text recognition branch, we integrate attention mechanisms and long short-term memory networks as the basic network for Japanese character text recognition. In addition, we set up separate text feature recognition systems for Japanese computer writing and handwriting to prevent feature overlap problems. For Japanese voice sequence recognition, we used a combination of memory gating unit and encoder, based on the network still extending the structure of the deep neural network and using the residual structure connection in the gating unit to avoid the gradient disappearance problem. At the end of the system, we use a softmax layer to connect the text recognition and voice recognition networks to form a Japanese language teaching aid system. To verify the efficiency of our system, we selected the Japanese text recognition public dataset and voice recognition public dataset for experimental validation. To match the practical application of the system, we created our dataset based on the dataset standard and conducted experimental validation. To compare other Japanese recognition methods, we selected the six most representative Japanese recognition algorithms for experimental comparison. To ensure the balance of the experiments, each algorithm is trained in a separate experimental environment for modeling and tuning parameters. Experimental performance and the experimental results show that our method significantly outperforms the other methods and has better system stability.

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Section: Resultsmentioning

confidence: 99%

Hybrid Japanese Language Teaching Aid System with Multi-Source Information Fusion Mapping

Zhang

2022

Mathematical Problems in Engineering

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“…The HCR's objective is to transfer the handwritten text documents from digital images to encode characters that are readable and editable by using the word processing application [2,3]. The HCR is used in various applications such as tracking of vehicle number plates, input from paper, comprehensible handwriting, signature verification, signboard reading, and touch screen device [4]. HCR is one of the important applications in the research of pattern recognition which improves the friendliness and socialization of mobile devices and networks.…”

Section: Introductionmentioning

confidence: 99%

Handwritten Character Recognition Using Morlet Stacked Sparse Auto Encoder based Feature Dimension Reduction

2023

IJIES

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Handwritten character recognition (HCR) is a growing field in the applications of pattern recognition, image processing, communication technologies, and so on. But, the identification of handwritten characters affected because of different styles of writers, or even one writer's style differed according to the conditions. Moreover, the huge amount of features from the handwritten characters also affect the classification. To address the aforementioned issues, the hybrid feature extraction (HFE) with morlet stacked sparse auto-encoder (MSSAE) based feature dimensionality reduction is proposed for improving the classification of HCR. Here, the HFE is the combination of different textural and shape features such as histogram of oriented gradients (HOG), gray-level co-occurrence matrix (GLCM), discrete wavelet transform (DWT), and skeleton features. The morlet wavelet activation function is used in the MSSAE to enhance the dimensionality reduction ability that used for an effective reduction of feature dimensions. The reduction of feature dimension using MSSAE is used to improve the classification using multi-class support vector machine (MSVM). In this research, the real-time indigenous languages i.e., English and Kannada handwritten characters from the Chars74K dataset are used for the analysis i.e., offline recognition. On the other hand, the online recognition of Kannada characters is also done for analyzing the HFE-MSSAE method. The HFE-MSSAE method is analyzed in terms of accuracy, precision, recall, CSI and F-measure. The existing researches namely convolutional neural network (CNN), hybrid feature based long short term memory (HF-LSTM) and elephant herding optimization -long short term memory (EHO-LSTM) are used to evaluate the HFE-MSSAE method. The accuracy of the HFE-MSSAE for Kannada is 96.73% which is higher than the HF-LSTM and EHO-LSTM.

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“…Therefore, the hybrid neural network composed of RNN and CNN can make up for the above shortcomings and better capture the spatiotemporal dynamic characteristics contained in the nonlinear system data. However, so far, the research on the hybrid neural network composed of RNN and CNN mainly focused on image recognition [40][41][42], emotion analysis [43][44][45], and text recognition [46][47], but its research in MPC and nonlinear system modeling has not been found.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based State-Dependent ARX Modeling and Predictive Control of Nonlinear Systems

Kang

Peng

et al. 2023

IEEE Access

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For many practical industrial objects with time-varying operating points, strong nonlinearity, and difficulty in obtaining analytical models, the data-driven identification method is usually used to model such nonlinear systems. However, it is difficult for traditional modeling algorithms to effectively extract the dynamic characteristics of nonlinear systems from data and obtain accurate mathematical models. In this paper, we consider using the deep learning network combined with the state-dependent exogenous variable autoregressive (SD-ARX) model framework to build the nonlinear system model, so as to effectively and accurately learn the space-time characteristics of the nonlinear system from the sample data. Based on the idea, the hybrid models, i.e., the RNN-ARX model, CNN-ARX model, and RNN-CNN-ARX model are built, which use recurrent neural networks (RNN), convolutional neural networks (CNN) and their combination to fit the function-type coefficients of SD-ARX model, respectively. SD-ARX model based on deep learning has the advantages of local linearity and global nonlinearity. Compared with the other two models, the RNN-CNN-ARX model has a stronger ability to extract the multidimensional spatiotemporal dynamic characteristics of nonlinear systems, because it combines the advantages of RNN in mining temporal features and CNN in extracting spatial features. According to the structural characteristics of these models, three model-based predictive control (MPC) strategies are designed, i.e., RNN-ARX-MPC, CNN-ARX-MPC, and RNN-CNN-ARX-MPC. The real-time control comparative experiment on an actual multiwater-tank object shows that the proposed modeling and MPC method is feasible and effective for the modeling and predictive control of the nonlinear system.

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RETRACTED ARTICLE: Effective offline handwritten text recognition model based on a sequence-to-sequence approach with CNN–RNN networks

Cited by 32 publications

References 17 publications

Hybrid Japanese Language Teaching Aid System with Multi-Source Information Fusion Mapping

Hybrid Japanese Language Teaching Aid System with Multi-Source Information Fusion Mapping

Handwritten Character Recognition Using Morlet Stacked Sparse Auto Encoder based Feature Dimension Reduction

Deep Learning-Based State-Dependent ARX Modeling and Predictive Control of Nonlinear Systems

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