## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Scheme 1.**} & \multirow{2}{*}{**Particular**} & \multirow{2}{*}{**Features Used**} & \multirow{2}{*}{**Classif. Scheme**} & \multirow{2}{*}{**No. of Test Samples**} & \multirow{2}{*}{**No. of Features Classif.**} & \multirow{2}{*}{**Max. Recogn. Test-Acc. (\%)**} & \multirow{2}{*}{**Data Set Used**} \\  & & & & & & & \\ \hline \multirow{4}{*}{1} & [PERSON] et al. & \multicolumn{3}{c}{Line fitting,} & \multirow{4}{*}{MLP} & \multirow{4}{*}{1568} & \multirow{4}{*}{296} & \multirow{4}{*}{92.8} & \multirow{4}{*}{[57]} \\  & & & & & & & \\  & 2008 [57] & & & & & & \\  & & code-based features & & & & & \\  & [PERSON] & Gradient, neighborhood & & & & & \\  & [PERSON] [58] & pixels weight, and distance & SVM & 25,000 & 192 & 94.3 & [58] \\  & & & transform-based features & & & & \\  & [PERSON] et al. & Curvelet transform-based & & & & & \\  & 2011 [59] & features & & KNN & 7965 & 190 & 93.8 & [59] \\  & & [PERSON] et al. & Wavelet transform-based features & & & & \\  & 2014 [27]. & features & & & & & \\  & 5 & [PERSON] et al. & Local auto-correlation of & & & & \\  & 2014 [60] & gradient & & & & & \\  & 6 & [PERSON] et al. & Hybrid of structural and & & & & \\  & 2015 [15]. & statistical features & & & & & \\  & & Chain code, zone-based & & & & & \\  & 7 & [PERSON] et al. & centroid, background & & & & \\  & 2015 [28]. & directional distribution and & & & & & \\  & & distance profile features & & & & & \\  & 8 & [PERSON] et al. & [PERSON] discriminant-based & & & & \\  & 2016 [17]. & features & & & & & \\  & 9 & [PERSON] et al. & CNN-based features & SVM & 3894 & 4096, 2560 & 95.180 & [63] \\  & & 2017 [30]. & & & & & \\  & 10 & [PERSON], & & & & & \\  & 2018 [16] & Histogram of gradients & MLP & 900 & 1224 & 97.5 & [16] \\  & 11 & [PERSON] et al. & HOG, convex hull, and & & & & \\  & 2019 [29] & longest run-based features & & SVM & 10,800 & 5568 & 94.15 & [55] \\  & & [PERSON] et al. & Projection profile-based & & Quadratic & & \\  & 2017 [64] & features with HOG & & SVM & 4428 & NA & 96.6 & [62] \\  & Proposed & Hybrid of BDWT and & & & & & \\  & scheme & DCNN-based features & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Summary of previous work done in the field with the proposed one. the classification error was limited to 1.27% only. The presented work will be helpful for further development of recognition models related to handwritten Hindi characters. In future, the proposed scheme could be extended by including some more advanced pre-trained deep convolutional networks like InceptionV4-net, ResNeXt, Squeeze-Excitation network (SE-Net), etc. for feature extraction. Other classification algorithms like KNN, RNN, LSTM, Random-forest, and Naive-Bayes could also be analyzed for the extended version. Conceptualization, D.R. ; Data curation, D.R. and O.P.M. ; Formal analysis, O.P.M. ; Investigation, D.R. ; Methodology, O.P.M. ; Supervision, A.R.G. and P.S. ; Validation, A.R.G. ; Writing-original draft, D.R. ; Writing-review & editing, A.R.G., H.H.A. and P.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable, the study does not report any data. The authors declare no conflict of interest.