A Sequential Handwriting Recognition Model Based on a Dynamically Configurable CRNN

Handwriting recognition refers to recognizing a handwritten input that includes character(s) or digit(s) based on an image. Because most applications of handwriting recognition in real life contain sequential text in various languages, there is a need to develop a dynamic handwriting recognition sys...

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Vydané v:Sensors (Basel, Switzerland) Ročník 21; číslo 21; s. 7306
Hlavní autori: AL-Saffar, Ahmed, Awang, Suryanti, AL-Saiagh, Wafaa, AL-Khaleefa, Ahmed Salih, Abed, Saad Adnan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 02.11.2021
MDPI
Predmet:
Artificial intelligence
Automation
Classification
Datasets
Deep learning
Genetic algorithms
Handwriting
handwriting recognition
Machine learning
metaheuristics optimization
Neural Architecture Search (NAS), configuration search
Neural networks
Optimization techniques
ISSN:1424-8220, 1424-8220
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Shrnutí:Handwriting recognition refers to recognizing a handwritten input that includes character(s) or digit(s) based on an image. Because most applications of handwriting recognition in real life contain sequential text in various languages, there is a need to develop a dynamic handwriting recognition system. Inspired by the neuroevolutionary technique, this paper proposes a Dynamically Configurable Convolutional Recurrent Neural Network (DC-CRNN) for the handwriting recognition sequence modeling task. The proposed DC-CRNN is based on the Salp Swarm Optimization Algorithm (SSA), which generates the optimal structure and hyperparameters for Convolutional Recurrent Neural Networks (CRNNs). In addition, we investigate two types of encoding techniques used to translate the output of optimization to a CRNN recognizer. Finally, we proposed a novel hybridized SSA with Late Acceptance Hill-Climbing (LAHC) to improve the exploitation process. We conducted our experiments on two well-known datasets, IAM and IFN/ENIT, which include both the Arabic and English languages. The experimental results have shown that LAHC significantly improves the SSA search process. Therefore, the proposed DC-CRNN outperforms the handcrafted CRNN methods.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s21217306