Implementation of Residual Network (ResNet) for Devanagari Handwritten Character Recognition

Mhapsekar, Mandar; Mhapsekar, Prathamesh; Mhatre, Aniket; Sawant, Vinaya

doi:10.1007/978-981-15-3242-9_14

Cited by 21 publications

(8 citation statements)

References 5 publications

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“…Although it is the basic version of GoogLeNet and Inception V3, the performance would be highly impacted by the quality of input imagery, size, and robustness of the dataset, especially for the agricultural environments. ResNet50 addresses the neural network degradation problem by introducing identity mapping, which results in the disappearance of gradient parameters and the non-ideal convergence effect on the deeper networks [ 66 , 67 ]. This feature contributed to the enhanced performance of ResNet50 compared to the other models thereby suggesting the suitability of ResNet50 for agricultural applications for various crop biotic and abiotic stress characterizations.…”

Section: Discussionmentioning

confidence: 99%

Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery

Chandel

Rajwade

Dubey

et al. 2022

Plants

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Timely crop water stress detection can help precision irrigation management and minimize yield loss. A two-year study was conducted on non-invasive winter wheat water stress monitoring using state-of-the-art computer vision and thermal-RGB imagery inputs. Field treatment plots were irrigated using two irrigation systems (flood and sprinkler) at four rates (100, 75, 50, and 25% of crop evapotranspiration [ETc]). A total of 3200 images under different treatments were captured at critical growth stages, that is, 20, 35, 70, 95, and 108 days after sowing using a custom-developed thermal-RGB imaging system. Crop and soil response measurements of canopy temperature (Tc), relative water content (RWC), soil moisture content (SMC), and relative humidity (RH) were significantly affected by the irrigation treatments showing the lowest Tc (22.5 ± 2 °C), and highest RWC (90%) and SMC (25.7 ± 2.2%) for 100% ETc, and highest Tc (28 ± 3 °C), and lowest RWC (74%) and SMC (20.5 ± 3.1%) for 25% ETc. The RGB and thermal imagery were then used as inputs to feature-extraction-based deep learning models (AlexNet, GoogLeNet, Inception V3, MobileNet V2, ResNet50) while, RWC, SMC, Tc, and RH were the inputs to function-approximation models (Artificial Neural Network (ANN), Kernel Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM) and Long Short-Term Memory (DL-LSTM)) to classify stressed/non-stressed crops. Among the feature extraction-based models, ResNet50 outperformed other models showing a discriminant accuracy of 96.9% with RGB and 98.4% with thermal imagery inputs. Overall, classification accuracy was higher for thermal imagery compared to RGB imagery inputs. The DL-LSTM had the highest discriminant accuracy of 96.7% and less error among the function approximation-based models for classifying stress/non-stress. The study suggests that computer vision coupled with thermal-RGB imagery can be instrumental in high-throughput mitigation and management of crop water stress.

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Section: Discussionmentioning

confidence: 99%

Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery

Chandel

Rajwade

Dubey

et al. 2022

Plants

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“…A CNN model [25][26][27][28][29][30][31][32][33][34] is a series of convolution layers followed by fully connected layers. Convolution layers allow the extraction of important features from the input data.…”

Section: Convolution Neural Network Architecturementioning

confidence: 99%

Improved Arabic Alphabet Characters Classification Using Convolutional Neural Networks (CNN)

Wagaa

Kallel

Mellouli

2022

Computational Intelligence and Neuroscience

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Handwritten characters recognition is a challenging research topic. A lot of works have been present to recognize letters of different languages. The availability of Arabic handwritten characters databases is limited. Motivated by this topic of research, we propose a convolution neural network for the classification of Arabic handwritten letters. Also, seven optimization algorithms are performed, and the best algorithm is reported. Faced with few available Arabic handwritten datasets, various data augmentation techniques are implemented to improve the robustness needed for the convolution neural network model. The proposed model is improved by using the dropout regularization method to avoid data overfitting problems. Moreover, suitable change is presented in the choice of optimization algorithms and data augmentation approaches to achieve a good performance. The model has been trained on two Arabic handwritten characters datasets AHCD and Hijja. The proposed algorithm achieved high recognition accuracy of 98.48% and 91.24% on AHCD and Hijja, respectively, outperforming other state-of-the-art models.

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“…For the Bangla script datasets, viz, D1, D2, D3, and D4, the deep learning based techniques used to compare our results are unified CNN [40], AlexNet [41], DenseNet [43], Multi-column Multi-scale CNN (MMCNN) [45], Residual Network (ResNet-50) [47], BornoNet [48], and modified ResNet-18 [49]. Moreover, for the Devanagari script datasets, viz, D5 and D6, the deep learning based techniques used to compare our results are MMCNN [45], CNN [50], ResNet-50 [51], and Inception V3 [52]. Furthermore, the classical machine learning based techniques used to compare our results are Sparse Concept Coding with Tetrolet transform and Nearest Neighbor method [42], Oppositionbased Multiobjective Harmony Search with SVM [44], and Advanced Feature Sets with SVM [46].…”

Section: 69mentioning

confidence: 99%

Quantum Particle Swarm Optimization Based Convolutional Neural Network for Handwritten Script Recognition

Sharma¹,

Kaushik²,

Gondhi³

et al. 2022

Computers, Materials &Amp; Continua

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Even though several advances have been made in recent years, handwritten script recognition is still a challenging task in the pattern recognition domain. This field has gained much interest lately due to its diverse application potentials. Nowadays, different methods are available for automatic script recognition. Among most of the reported script recognition techniques, deep neural networks have achieved impressive results and outperformed the classical machine learning algorithms. However, the process of designing such networks right from scratch intuitively appears to incur a significant amount of trial and error, which renders them unfeasible. This approach often requires manual intervention with domain expertise which consumes substantial time and computational resources. To alleviate this shortcoming, this paper proposes a new neural architecture search approach based on meta-heuristic quantum particle swarm optimization (QPSO), which is capable of automatically evolving the meaningful convolutional neural network (CNN) topologies. The computational experiments have been conducted on eight different datasets belonging to three popular Indic scripts, namely Bangla, Devanagari, and Dogri, consisting of handwritten characters and digits. Empirically, the results imply that the proposed QPSO-CNN algorithm outperforms the classical and state-of-the-art methods with faster prediction and higher accuracy.

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Implementation of Residual Network (ResNet) for Devanagari Handwritten Character Recognition

Cited by 21 publications

References 5 publications

Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery

Water Stress Identification of Winter Wheat Crop with State-of-the-Art AI Techniques and High-Resolution Thermal-RGB Imagery

Improved Arabic Alphabet Characters Classification Using Convolutional Neural Networks (CNN)

Quantum Particle Swarm Optimization Based Convolutional Neural Network for Handwritten Script Recognition

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