Devanagari Character Recognition: A Comprehensive Literature Review
The Devanagari script originated from the ancient Brahmi script and is a widely used Indic script for writing different languages, like Sanskrit, Hindi, Marathi, Nepali, and Konkani. Recognizing handwritten Devanagari characters poses significant challenges due to their complexity and handwriting va...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10807209/ |
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| author | Sandhya Arora Latesh Malik Sonakshi Goyal Debotosh Bhattacharjee Mita Nasipuri Ondrej Krejcar |
| author_facet | Sandhya Arora Latesh Malik Sonakshi Goyal Debotosh Bhattacharjee Mita Nasipuri Ondrej Krejcar |
| author_sort | Sandhya Arora |
| collection | DOAJ |
| description | The Devanagari script originated from the ancient Brahmi script and is a widely used Indic script for writing different languages, like Sanskrit, Hindi, Marathi, Nepali, and Konkani. Recognizing handwritten Devanagari characters poses significant challenges due to their complexity and handwriting variability. This literature review examines the evolution of handwritten Devanagari character recognition (HDCR), exploring early template matching and feature extraction methods that struggled with the script’s intricacy. Advances introduced structural and statistical techniques, improving accuracy by analyzing geometric properties and patterns. The advent of machine learning, particularly deep learning, revolutionized HDCR with convolutional neural networks (CNNs) and recurrent neural networks (RNNs), significantly enhancing performance. Hybrid approaches that combine multiple techniques have shown promising results, balancing accuracy and computational complexity. Challenges remain, including handwriting variability, noise, and the need for real-time performance. The lack of large, diverse datasets for training and evaluation is a significant hurdle. This review highlights efforts to create annotated datasets and benchmarks, providing a comprehensive overview of HDCR methodologies, strengths, limitations, and future research directions. These insights aim to advance HDCR, contributing to more accurate and efficient recognition systems and enhancing digital text processing for linguistic, educational, and archival purposes. |
| format | Article |
| id | doaj-art-8e7bb55adecc48baaae8c7911df02e9d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8e7bb55adecc48baaae8c7911df02e9d2025-01-03T00:01:50ZengIEEEIEEE Access2169-35362025-01-01131249128410.1109/ACCESS.2024.352024810807209Devanagari Character Recognition: A Comprehensive Literature ReviewSandhya Arora0https://orcid.org/0000-0002-6412-7831Latesh Malik1https://orcid.org/0000-0001-5660-9438Sonakshi Goyal2https://orcid.org/0009-0004-6410-4981Debotosh Bhattacharjee3Mita Nasipuri4https://orcid.org/0000-0002-3906-5309Ondrej Krejcar5https://orcid.org/0000-0002-5992-2574Cummins College of Engineering for Women, Pune, IndiaGovernment College of Engineering, Nagpur, IndiaCummins College of Engineering for Women, Pune, IndiaDepartment of Computer Science and Engineering, Jadavpur University, Kolkata, IndiaDepartment of Computer Science and Engineering, Jadavpur University, Kolkata, IndiaResearch Center, Skoda Auto University, Mlada Boleslav, Czech RepublicThe Devanagari script originated from the ancient Brahmi script and is a widely used Indic script for writing different languages, like Sanskrit, Hindi, Marathi, Nepali, and Konkani. Recognizing handwritten Devanagari characters poses significant challenges due to their complexity and handwriting variability. This literature review examines the evolution of handwritten Devanagari character recognition (HDCR), exploring early template matching and feature extraction methods that struggled with the script’s intricacy. Advances introduced structural and statistical techniques, improving accuracy by analyzing geometric properties and patterns. The advent of machine learning, particularly deep learning, revolutionized HDCR with convolutional neural networks (CNNs) and recurrent neural networks (RNNs), significantly enhancing performance. Hybrid approaches that combine multiple techniques have shown promising results, balancing accuracy and computational complexity. Challenges remain, including handwriting variability, noise, and the need for real-time performance. The lack of large, diverse datasets for training and evaluation is a significant hurdle. This review highlights efforts to create annotated datasets and benchmarks, providing a comprehensive overview of HDCR methodologies, strengths, limitations, and future research directions. These insights aim to advance HDCR, contributing to more accurate and efficient recognition systems and enhancing digital text processing for linguistic, educational, and archival purposes.https://ieeexplore.ieee.org/document/10807209/OCRhandwritten Devanagari character recognitionmachine learningdeep learning |
| spellingShingle | Sandhya Arora Latesh Malik Sonakshi Goyal Debotosh Bhattacharjee Mita Nasipuri Ondrej Krejcar Devanagari Character Recognition: A Comprehensive Literature Review IEEE Access OCR handwritten Devanagari character recognition machine learning deep learning |
| title | Devanagari Character Recognition: A Comprehensive Literature Review |
| title_full | Devanagari Character Recognition: A Comprehensive Literature Review |
| title_fullStr | Devanagari Character Recognition: A Comprehensive Literature Review |
| title_full_unstemmed | Devanagari Character Recognition: A Comprehensive Literature Review |
| title_short | Devanagari Character Recognition: A Comprehensive Literature Review |
| title_sort | devanagari character recognition a comprehensive literature review |
| topic | OCR handwritten Devanagari character recognition machine learning deep learning |
| url | https://ieeexplore.ieee.org/document/10807209/ |
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