Huruf: An Application for Arabic Handwritten Character Recognition Using Deep Learning

Kamal, Minhaz; Shaiara, Fairuz; Abdullah, Chowdhury Mohammad; Ahmed, Shamsuddin; Ahmed, Tasnim; Kabir, Md. Hasanul

doi:10.1109/iccit57492.2022.10054769

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…Moreover, the authors [28] proposed a pipeline system consists of 18 layers, including four layers each for convolution, pooling, batch normalization, dropout, and one Global average pooling and Dense layer. Hyperparameters such as optimizer, kernel initializer, and activation function were carefully examined.…”

Section: Results Discussionmentioning

confidence: 99%

Real time handwriting recognition system using CNN algorithms

Maryam Yaseen Abdullah

2023

WJCMS

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Abstract— The growing use of digital technologies across various sectors and daily activities has made handwriting recognition a popular research topic. Despite the continued relevance of handwriting, people still require the conversion of handwritten copies into digital versions that can be stored and shared digitally. Handwriting recognition involves the computer's strength to identify and understand legible handwriting input data from various sources, including document, photo-graphs and others. Handwriting recognition pose a complexity challenge due to the diversity in handwriting styles among different individuals especially in real time applications. In this paper, an automatic system was designed to handwriting recognition using the recent artificial intelligent algorithms, the conventional neural network (CNN). Different CNN models were tested and modified to produce a system has two important features high performance accuracy and less testing time. These features are the most important factors for real time applications. The experimental results were conducted on a dataset includes over 400,000 handwritten names; the best performance accuracy results were 99.8% for SqueezeNet model.

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Section: Results Discussionmentioning

confidence: 99%

Real time handwriting recognition system using CNN algorithms

Maryam Yaseen Abdullah

2023

WJCMS

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“…Recognition , especially when dealing with datasets containing characters and diverse writing styles, this is shown in a study [7] and [16] and [17].…”

Section: Ccns Demonstrated High Efficacy In Arabic Handwrittenmentioning

confidence: 99%

A Deep Learning Approach to Convert Handwritten Arabic Text to Digital Form

Alshahrani,

Alghamdi

2024

ijacsa

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The recognition of Arabic words presents considerable difficulties owing to the complex characteristics of the Arabic script, which encompasses letters positioned both above and below the baseline, hamzas, and dots. In order to address these intricacies, we provide a structured approach for transforming handwritten Arabic text into a digital format. We employ a hybrid deep learning technique that combines Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (BLSTM), and Connectionist Temporal Classification (CTC). We collected datasets that cover a wide range of Arabic text variations. We have also created a pre-processing pipeline. Our methodology successfully achieved an accuracy rate of 99.52%. At the level of recognizing the letters of the word, with an accuracy of 98.36% at the level of the full word. In order to evaluate the effectiveness of our suggested method for recognizing handwritten text, we utilize two essential metrics: Word Error Rate (WER) and Character Error Rate (CER) to compare its performance. The experimental research demonstrates a WER of 1.64 % and a CER of 0.48%.

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“…After analyzing 40 and 256 data sets, the CNN achieved an accuracy rate of 97.2%. Kamal et al [21], the proposed 18-layer pipeline, which includes convolution, pooling, batch normalization, dropout, global average pooling, and dense layers, achieved 96.93% accuracy on AHCD and MadBase datasets, making it suitable for real-world applications.…”

Section: Related Workmentioning

confidence: 99%

The Impact of Various Factors on the Convolutional Neural Networks Model on Arabic Handwritten Character Recognition

Alsayed,

Li,

Fat’hAlalim

et al. 2024

ijacsa

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Recognizing Arabic handwritten characters (AHCR) poses a significant challenge due to the intricate and variable nature of the Arabic script. However, recent advancements in machine learning, particularly through Convolutional Neural Networks (CNNs), have demonstrated promising outcomes in accurately identifying and categorizing these characters. While numerous studies have explored languages like English and Chinese, the Arabic language still requires further research to enhance its compatibility with computer systems. This study investigates the impact of various factors on the CNN model for AHCR, including batch size, filter size, the number of blocks, and the number of convolutional layers within each block. A series of experiments were conducted to determine the optimal model configuration for the AHCD dataset. The most effective model was identified with the following parameters: Batch Size (BS) = 64, Number of Blocks (NB) = 3, Number of Convolution Layers in Block (NC) = 3, and Filter Size (FS) = 64. This model achieved an impressive training accuracy of 98.29% and testing accuracy of 97.87%.

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Huruf: An Application for Arabic Handwritten Character Recognition Using Deep Learning

Cited by 7 publications

References 19 publications

Real time handwriting recognition system using CNN algorithms

Real time handwriting recognition system using CNN algorithms

A Deep Learning Approach to Convert Handwritten Arabic Text to Digital Form

The Impact of Various Factors on the Convolutional Neural Networks Model on Arabic Handwritten Character Recognition

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