RNN based online handwritten word recognition in Devanagari and Bengali scripts using horizontal zoning

Ghosh, Rajib; Vamshi, Chirumavila; Kumar, Prabhat

doi:10.1016/j.patcog.2019.03.030

Cited by 77 publications

(19 citation statements)

References 16 publications

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“…Plant physiological state is gradual, which requires a neural network with a strong ability to remember historical information. The RNN model has been extensively studied in the field of language and text recognition [ 22 ]. The LSTM neural network model, optimized on the basis of RNN [ 23 ], has achieved good results in areas such as semantic analysis and image recognition that require strong historical information memory [ 24 , 25 , 26 ], but it is rarely used in the field of physiological data analysis.…”

Section: Introductionmentioning

confidence: 99%

A Time Series Data Filling Method Based on LSTM—Taking the Stem Moisture as an Example

Song

Gao

Zhao

et al. 2020

Sensors

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In order to solve the problem of data loss in sensor data collection, this paper took the stem moisture data of plants as the object, and compared the filling value of missing data in the same data segment with different data filling methods to verify the validity and accuracy of the stem water filling data of the LSTM (Long Short-Term Memory) model. This paper compared the accuracy of missing stem water data for plants under different data filling methods to solve the problem of data loss in sensor data collection. Original stem moisture data was selected from Lagerstroemia Indica which was planted in the Haidian District of Beijing in June 2017. Part of the data which treated as missing data was manually deleted. Interpolation methods, time series statistical methods, the RNN (Recurrent Neural Network), and LSTM neural network were used to fill in the missing part and the filling results were compared with the original data. The result shows that the LSTM has more accurate performance than the RNN. The error values of the bidirectional LSTM model are the smallest among several models. The error values of the bidirectional LSTM are much lower than other methods. The MAPE (mean absolute percent error) of the bidirectional LSTM model is 1.813%. After increasing the length of the training data, the results further proved the effectiveness of the model. Further, in order to solve the problem of one-dimensional filling error accumulation, the LSTM model is used to conduct the multi-dimensional filling experiment with environmental data. After comparing the filling results of different environmental parameters, three environmental parameters of air humidity, photosynthetic active radiation, and soil temperature were selected as input. The results show that the multi-dimensional filling can greatly extend the sequence length while maintaining the accuracy, and make up for the defect that the one-dimensional filling accumulates errors with the increase of the sequence. The minimum MAPE of multidimensional filling is 1.499%. In conclusion, the data filling method based on LSTM neural network has a great advantage in filling the long-lost time series data which would provide a new idea for data filling.

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Section: Introductionmentioning

confidence: 99%

A Time Series Data Filling Method Based on LSTM—Taking the Stem Moisture as an Example

Song

Gao

Zhao

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Such deep learning has been designed as feedforward, but recently, it has been used as a recurrent neural network (RNN) in which a deep structure is converted into a circular structure. The RNN is a structure that can repeatedly execute the hidden layer between the input layer and the output layer, so it can receive data sequentially over time in repeated processing [19,20]. Due to this structure, it is suitable for the learning of time series data.…”

Section: Deep Learning Technique For Time Series Analysismentioning

confidence: 99%

Urban traffic accident risk prediction for knowledge-based mobile multimedia service

Park

Hong

2020

Pers Ubiquit Comput

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Traditional accident prediction models have been mostly designed with statistical analysis that finds and analyzes the causal relationships between traffic accidents and a variety of human, road geometry, and environmental factors. However, these statistical methods have limitations in that they are based on assumptions about data distribution and function type. Therefore, this study suggests an accident prediction model using deep learning. This newly suggested risk prediction model is for predicting risk by reflecting static features of the road, such its length and the speed limit on it, and dynamic features of the road, such as traffic volume when driving on it, and the altitude and azimuth of the sun. For this purpose, 4470 accident cases, collected over 5 months from August to December 2018 in Seoul-the most complex, high-traffic, and accident-prone city in Korea-were analyzed. As a result of testing the model using such data, it was found to have an accuracy of 75% and recall of 81%. Based on testing results for the suggested risk prediction model, a system was developed to guide not only accident-prone regions predicted using statistical data but to also guide a risk level for the road. This level of risk is estimated based upon each given situation, so it can change even for the same road. This guide system can be used to provide a level of risk for each road segment and region but also to improve roads with recommendations, such as installation of safety features. In addition, it could support a mobile system that provides a driver with the optimized driving path for safety.

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“…Recurrent neural networks (RNN) have been proved to excel at various sequential tasks, such as speech recognition [79], speech synthesis [80], handwriting recognition [81], and image to text [82]. Particularly, Long Short-Term Memory (LSTM) layers [83], transformers and self-attention mechanism [84] are the robust architecture for modelling long range sequence data with auto correlations like time series data, natural languages etc.…”

Section: Figure 1: Distribution Of Different Type Of Datasets (A) Datmentioning

confidence: 99%

A Survey on Generative Adversarial Networks for imbalance problems in computer vision tasks

Sampath

Maurtua

Martín

et al. 2020

Preprint

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Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets.In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examines key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms.

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RNN based online handwritten word recognition in Devanagari and Bengali scripts using horizontal zoning

Cited by 77 publications

References 16 publications

A Time Series Data Filling Method Based on LSTM—Taking the Stem Moisture as an Example

A Time Series Data Filling Method Based on LSTM—Taking the Stem Moisture as an Example

Urban traffic accident risk prediction for knowledge-based mobile multimedia service

A Survey on Generative Adversarial Networks for imbalance problems in computer vision tasks

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