Handwritten Bangla Digit Recognition Using Chemical Reaction Optimization

Boni, Pritam Khan; Abir, Bappy Shahriar; Hasan, Haswadi; Islam, Md. Rafiqul

doi:10.1109/icccnt.2018.8494202

Cited by 3 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With a batch size of 64, 30 epochs have been completed in CNN. Two convolutional (Hassan, 2015) 0.9670 SVM+GA 0.9770 SVM+NSGA (Sarkhel, 2016) 0.9780 CNN+DBN (Alom, 2017) 0.9878 SVM+CRO (Boni, 2018) 0.9896 Proposed Model 0.9910 SVM+QTS (Roy, 2012) ISI 0.9735 PCA+SVM (Wen, 2007) 0.9505 SVM+NSGA (Sarkhel, 2016) 0.9823 Proposed Model 0.9844 ACMA (Shopon, 2016) Train: ISI Test: CMATERdb 0.9950 Proposed Model 0.9980…”

Section: Resultsmentioning

confidence: 99%

Bangla Handwritten Numeral Recognition Using Deep Convolutional Neural Network

2022

View full text Add to dashboard Cite

The recognition of Bangla handwritten numerals (BHNR) has recently emerged as a very interesting area for machine learning and pattern recognition research. Recently, the technology for character or object recognition has also advanced. Bangla handwritten number recognition can serve as a foundation for creating an Optical Character Recognition (OCR) in the Bangla language. However, the lack of a sizable and accurate dataset makes Bangla's handwritten numeral recognition study insufficient in contrast to that of other well-known languages. Similar to MNIST for English digits, NumtaDB is by far the largest dataset collection for handwritten digits in the Bangla language. The most used datasets for the recognition of Bangla handwritten numerals in the past were NumtaDB, CMATERdb, and ISI. The majority of approaches now in use rely on feature extraction and a few outdated machine learning algorithms. Although some approaches operate quickly enough to meet practical demands, they are not always accurate. Even while certain techniques work quite well for languages other than Bangla, they still require improvement. Convolutional Neural Networks (CNN), in particular, are demonstrating excellent achievements in this discipline with higher accuracy. In this work, we’ve used custom CNN architectures to build our model to recognize digits using all the existing datasets with a high degree of accuracy. Our CNN model shows an average of 98% accuracy recognizing Bangla numeric in respect of above datasets. We have cross verified our model with mixed datasets and the result is also promising.

show abstract

Section: Resultsmentioning

confidence: 99%

Bangla Handwritten Numeral Recognition Using Deep Convolutional Neural Network

2022

View full text Add to dashboard Cite

show abstract

“…Supplementary material at https://docs.google.com/document/d/1kNA-NVSVpNUc46p c1Zg_K1sXD8vMCrWE/edit?usp5sharing&ouid5106536917224200212284&rtpof5true& sd5true shows the previous studies in handwritten digits recognition [19][20][21][22][23][24][25][26][27]. The past research focused more on the use of machine learning algorithms such as artificial neural network (ANN), convolution neural network (CNN), k-nearest neighbor (KNN) and correlation features selection (CFS) in building the handwritten digits recognition predictive model.…”

Section: Feature Selection In Handwritten Digit Recognitionmentioning

confidence: 98%

Improving handwritten digit recognition using hybrid feature selection algorithm

Chin

Lem

Wong

2022

ACI

View full text Add to dashboard Cite

PurposeThe amount of features in handwritten digit data is often very large due to the different aspects in personal handwriting, leading to high-dimensional data. Therefore, the employment of a feature selection algorithm becomes crucial for successful classification modeling, because the inclusion of irrelevant or redundant features can mislead the modeling algorithms, resulting in overfitting and decrease in efficiency.Design/methodology/approachThe minimum redundancy and maximum relevance (mRMR) and the recursive feature elimination (RFE) are two frequently used feature selection algorithms. While mRMR is capable of identifying a subset of features that are highly relevant to the targeted classification variable, mRMR still carries the weakness of capturing redundant features along with the algorithm. On the other hand, RFE is flawed by the fact that those features selected by RFE are not ranked by importance, albeit RFE can effectively eliminate the less important features and exclude redundant features.FindingsThe hybrid method was exemplified in a binary classification between digits “4” and “9” and between digits “6” and “8” from a multiple features dataset. The result showed that the hybrid mRMR + support vector machine recursive feature elimination (SVMRFE) is better than both the sole support vector machine (SVM) and mRMR.Originality/valueIn view of the respective strength and deficiency mRMR and RFE, this study combined both these methods and used an SVM as the underlying classifier anticipating the mRMR to make an excellent complement to the SVMRFE.

show abstract

Handwritten Numerals' Recognition in Kurdish Language Using Double Feature Selection

Abdulrazzaq

Khalaf

2019

2019 2nd International Conference on Engineering Technology and Its Applications (IICETA)

View full text Add to dashboard Cite

Handwritten Bangla Digit Recognition Using Chemical Reaction Optimization

Cited by 3 publications

References 16 publications

Bangla Handwritten Numeral Recognition Using Deep Convolutional Neural Network

Bangla Handwritten Numeral Recognition Using Deep Convolutional Neural Network

Improving handwritten digit recognition using hybrid feature selection algorithm

Handwritten Numerals' Recognition in Kurdish Language Using Double Feature Selection

Contact Info

Product

Resources

About