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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/32663/ http://umpir.ump.edu.my/id/eprint/32663/1/A%20sequential%20handwriting%20recognition%20model%20based%20on%20a%20dynamically.pdf |
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| author | Al-Saffar, Ahmed Suryanti, Awang Al-Saiagh, Wafaa Al-Khaleefa, Ahmed Salih Abed, Saad Adnan |
| author_facet | Al-Saffar, Ahmed Suryanti, Awang Al-Saiagh, Wafaa Al-Khaleefa, Ahmed Salih Abed, Saad Adnan |
| author_sort | Al-Saffar, Ahmed |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:07:20Z |
| format | Article |
| id | ump-32663 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:07:20Z |
| publishDate | 2021 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-326632024-01-03T06:40:59Z http://umpir.ump.edu.my/id/eprint/32663/ A sequential handwriting recognition model based on a dynamically configurable CRNN Al-Saffar, Ahmed Suryanti, Awang Al-Saiagh, Wafaa Al-Khaleefa, Ahmed Salih Abed, Saad Adnan QA76 Computer software 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. MDPI 2021-11-02 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/32663/1/A%20sequential%20handwriting%20recognition%20model%20based%20on%20a%20dynamically.pdf Al-Saffar, Ahmed and Suryanti, Awang and Al-Saiagh, Wafaa and Al-Khaleefa, Ahmed Salih and Abed, Saad Adnan (2021) A sequential handwriting recognition model based on a dynamically configurable CRNN. Sensors, 21 (21). pp. 1-25. ISSN 1424-8220. (Published) https://doi.org/10.3390/s21217306 https://doi.org/10.3390/s21217306 |
| spellingShingle | QA76 Computer software Al-Saffar, Ahmed Suryanti, Awang Al-Saiagh, Wafaa Al-Khaleefa, Ahmed Salih Abed, Saad Adnan A sequential handwriting recognition model based on a dynamically configurable CRNN |
| title | A sequential handwriting recognition model based on a dynamically configurable CRNN |
| title_full | A sequential handwriting recognition model based on a dynamically configurable CRNN |
| title_fullStr | A sequential handwriting recognition model based on a dynamically configurable CRNN |
| title_full_unstemmed | A sequential handwriting recognition model based on a dynamically configurable CRNN |
| title_short | A sequential handwriting recognition model based on a dynamically configurable CRNN |
| title_sort | sequential handwriting recognition model based on a dynamically configurable crnn |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/32663/ http://umpir.ump.edu.my/id/eprint/32663/ http://umpir.ump.edu.my/id/eprint/32663/ http://umpir.ump.edu.my/id/eprint/32663/1/A%20sequential%20handwriting%20recognition%20model%20based%20on%20a%20dynamically.pdf |