Hybrid CNN-SVM Classifier for Handwritten Digit Recognition

Ahlawat, Savita; Choudhary, Amit

doi:10.1016/j.procs.2020.03.309

Cited by 154 publications

(74 citation statements)

References 37 publications

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“…On the other hand, the obtained quantitative recognition performance metrics’ precision, recall, and F-measure on this benchmark had the same value of 0.9968%. These results surpassed that of many recent dedicated state-of-the-arts methods [ 101 , 102 , 103 , 104 ] evaluated on this database.…”

Section: Experiments and Discussionmentioning

confidence: 63%

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Ahmed

Gogate

Tahir

et al. 2021

Entropy

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Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing. However, OAHR continues to face several challenges, including the high variability of the Arabic script and its intrinsic characteristics such as cursiveness, ligatures, and diacritics, the unlimited variation in human handwriting, and the lack of large public databases. In this paper, we have introduced a novel context-aware model based on deep neural networks to address the challenges of recognizing offline handwritten Arabic text, including isolated digits, characters, and words. Specifically, we have proposed a supervised Convolutional Neural Network (CNN) model that contextually extracts optimal features and employs batch normalization and dropout regularization parameters to prevent overfitting and further enhance its generalization performance when compared to conventional deep learning models. We employed numerous deep stacked-convolutional layers to design the proposed Deep CNN (DCNN) architecture. The proposed model was extensively evaluated, and it was observed to achieve excellent classification accuracy when compared to the existing state-of-the-art OAHR approaches on a diverse set of six benchmark databases, including MADBase (Digits), CMATERDB (Digits), HACDB (Characters), SUST-ALT (Digits), SUST-ALT (Characters), and SUST-ALT (Names). Further comparative experiments were conducted on the respective databases using the pre-trained VGGNet-19 and Mobile-Net models; additionally, generalization capabilities experiments on another language database (i.e., MNIST English Digits) were conducted, which showed the superiority of the proposed DCNN model.

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Section: Experiments and Discussionmentioning

confidence: 63%

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Ahmed

Gogate

Tahir

et al. 2021

Entropy

View full text Add to dashboard Cite

show abstract

“…Different public handwritten document image datasets have been created and presented to resolve various document image challenges such as text line segmentation [7], word spotting [8], writer identification [9], digit and character segmentation and recognition [10][11][12], binarization [13], and a variety of other challenges [14][15][16]. These datasets enable researchers to develop automated and computationally efficient algorithms.…”

Section: Review Of Related Public Datasetsmentioning

confidence: 99%

SHIBR—The Swedish Historical Birth Records: a semi-annotated dataset

Cheddad

Kusetoğulları

Hilmkil³

et al. 2021

Neural Comput & Applic

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This paper presents a digital image dataset of historical handwritten birth records stored in the archives of several parishes across Sweden, together with the corresponding metadata that supports the evaluation of document analysis algorithms’ performance. The dataset is called SHIBR (the Swedish Historical Birth Records). The contribution of this paper is twofold. First, we believe it is the first and the largest Swedish dataset of its kind provided as open access (15,000 high-resolution colour images of the era between 1800 and 1840). We also perform some data mining of the dataset to uncover some statistics and facts that might be of interest and use to genealogists. Second, we provide a comprehensive survey of contemporary datasets in the field that are open to the public along with a compact review of word spotting techniques. The word transcription file contains 17 columns of information pertaining to each image (e.g., child’s first name, birth date, date of baptism, father's first/last name, mother’s first/last name, death records, town, job title of the father/mother, etc.). Moreover, we evaluate some deep learning models, pre-trained on two other renowned datasets, for word spotting in SHIBR. However, our dataset proved challenging due to the unique handwriting style. Therefore, the dataset could also be used for competitions dedicated to a large set of document analysis problems, including word spotting.

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“…Huang et al Huang et al [24] employed LSTM and SVM for drug-drug interaction (DDI) extraction, and the experimental results demonstrated the effectiveness of the proposed approach. To incorporate the advantages of SVM and CNN, Ahlawat et al [25] adopted a hybrid model for handwritten digit recognition.…”

Section: Related Workmentioning

confidence: 99%

A Two-Stage Biomedical Event Trigger Detection Method Based on Hybrid Neural Network and Sentence Embeddings

Ren²,

Tai³

et al. 2021

IEEE Access

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Biomedical event extraction is a challenging task in biomedical text mining, which plays an important role in improving biomedical research and disease prevention. As the crucial and prerequisite step in event extraction, biomedical trigger detection has attracted much attention. Previous approaches usually depended on feature engineering with unbalanced data. In this paper, we propose a two-stage method based on hybrid neural network for trigger detection, which divides trigger detection into recognition stage and classification stage. In the first stage, we build a BiLSTM based recognition model integrating attention mechanism (Att-BiLSTM). In the second stage, the classification model based on Passive-Aggressive online algorithm is constructed. Furthermore, to enrich sentence-level features, we establish sentence embeddings and add reading gate. On the multi-level event extraction (MLEE) corpus test dataset, our method achieves an F-score of 80.26%, which achieves the state-of-the-art systems.

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Hybrid CNN-SVM Classifier for Handwritten Digit Recognition

Cited by 154 publications

References 37 publications

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts

SHIBR—The Swedish Historical Birth Records: a semi-annotated dataset

A Two-Stage Biomedical Event Trigger Detection Method Based on Hybrid Neural Network and Sentence Embeddings

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