502 research outputs found
Huruf: An Application for Arabic Handwritten Character Recognition Using Deep Learning
Handwriting Recognition has been a field of great interest in the Artificial
Intelligence domain. Due to its broad use cases in real life, research has been
conducted widely on it. Prominent work has been done in this field focusing
mainly on Latin characters. However, the domain of Arabic handwritten character
recognition is still relatively unexplored. The inherent cursive nature of the
Arabic characters and variations in writing styles across individuals makes the
task even more challenging. We identified some probable reasons behind this and
proposed a lightweight Convolutional Neural Network-based architecture for
recognizing Arabic characters and digits. The proposed pipeline consists of a
total of 18 layers containing four layers each for convolution, pooling, batch
normalization, dropout, and finally one Global average pooling and a Dense
layer. Furthermore, we thoroughly investigated the different choices of
hyperparameters such as the choice of the optimizer, kernel initializer,
activation function, etc. Evaluating the proposed architecture on the publicly
available 'Arabic Handwritten Character Dataset (AHCD)' and 'Modified Arabic
handwritten digits Database (MadBase)' datasets, the proposed model
respectively achieved an accuracy of 96.93% and 99.35% which is comparable to
the state-of-the-art and makes it a suitable solution for real-life end-level
applications.Comment: Accepted in 25th ICCIT (6 pages, 4 tables, 4 figures
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