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Worddeepnet: handwritten gurumukhi word recognition using convolutional neural network

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Abstract

Deep learning models are considered a revolutionary learning paradigm in artificial intelligence and machine learning, piquing the interest of image recognition and computer vision experts. Because deep learning models have gained popularity and improved outcomes in the literature, this work provides a deep learning method based on a holistic approach to recognize offline handwritten Gurumukhi words. The holistic approach to word recognition treats a word as a separate entity rather than its component letters. Three characteristics are extracted from word pictures to train a Convolutional Neural Network (CNN), namely, zoning, diagonal, and centroid. Five performance measures are used to assess trained CNN performance, namely, Accuracy, True Positive Rate (TPR), False Positive Rate (FPR), Root Mean Square Error (RMSE), and Area Under Curve (AUC). The proposed model is trained and assessed using a 40,000 words benchmark dataset based on 70:30 partitioning technique, in which 70% of the data is used to train the model and 30% of the data is used to test the trained model. To assess the efficacy of the suggested technique, a fivefold cross validation process is performed. Using the partitioning method and cross-validation approach, the best accuracy rates of 95.11% and 94.96% are obtained after 30 epochs, respectively which surpassed the existing state-of-the-art offline handwritten Gurumukhi word recognition systems.

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Data availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Akal University, Talwandi Sabo, Punjab, India

    Harmandeep Kaur

  2. Arden University, Berlin, Germany

    Shally Bansal

  3. Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

    Munish Kumar

  4. University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Ajay Mittal & Krishan Kumar

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  1. Harmandeep Kaur
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Kaur, H., Bansal, S., Kumar, M. et al. Worddeepnet: handwritten gurumukhi word recognition using convolutional neural network. Multimed Tools Appl 82, 46763–46788 (2023). https://doi.org/10.1007/s11042-023-15527-2

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