FusionNet: A KNN-MLP Hybrid Model for Bengali Handwritten Digit Recognition using HOG and LBP Features

Anower Hossen, Mohammad Aman Ullah

Abstract


Recent years have seen a surge of interest in research related to Bengali handwritten digit recognition, largely driven by its significant practical relevance and the pervasive utilization of the Bengali language. Convolutional Neural Networks (CNNs) have demonstrated notable success in this domain; however, hybrid approaches that integrate handcrafted feature extraction with conventional machine learning classifiers are emerging as effective alternatives. This study proposes and evaluates FusionNet, a hybrid model that combines the strengths of feature-based and learning-based methods through a two-stage classification pipeline. First, an optimized K-Nearest Neighbors (KNN) classifier generates a coarse label prediction based on handcrafted features. This prediction is then incorporated with origainal feature then fed into a Multi-Layer Perceptron (MLP), which performs the final classification. To enhance the system's robustness and generalization, few preprocessing techniques such as, binarization, Otsu’s threshold, and data augmentation were implemented. Then, two complementary feature extraction techniques were applied. Firstly, Histogram of Oriented Gradients (HOG) is utilized; and secondly, Local Binary Patterns (LBP). These features were computed in parallel to mitigate runtime overhead, thereby enabling reduced runtime. FusionNet's performance was benchmarked against EfficientNet-B0, a state-of-the-art pre-trained CNN model, using two datasets: a custom dataset reflecting diverse handwriting styles and the publicly available NumtaDb dataset. FusionNet attained an accuracy of 87% on the custom dataset and 96% on NumtaDb. In comparison, EfficientNet-B0 achieved 91% and 97%, respectively. Although EfficientNet-B0 exhibited marginally superior accuracy, FusionNet exhibited superior efficiency and lower computational demands, thus rendering it a compelling candidate for deployment in resource-constrained environments.

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References


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DOI: https://doi.org/10.31449/inf.v49i4.9117

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