An Automated System for Recognizing Isolated Handwritten Bangla Characters using Deep Convolutional Neural Network

Hasan, Mahdi; Sultan, Rafi Ibn; Kasedullah, Mohammad

doi:10.1109/iscaie51753.2021.9431799

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…A lot of recognition progress can be found in the literature for both Hirakana and Katakana. Examples of excellent achievements in recognition accuracy rate are contributed in [11] for both Hirakana and Katakana, which are 98.83% and 98.19%, respectively.…”

Section: Syllabic Systemmentioning

confidence: 95%

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Construction of Alphabetic Character Recognition Systems: A Review

Majeed

Nariman

2023

UHD J SCI TECH

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Character recognition (CR) systems were attracted by a massive number of authors’ interest in this field, and lot of research has been proposed, developed, and published in this regard with different algorithms and techniques due to the great interest and demand of raising the accuracy of the recognition rate and the reliability of the presented system. This work is proposed to provide a guideline for CR system construction to afford a clear view to the authors on building their systems. All the required phases and steps have been listed and clarified within sections and subsections along with detailed graphs and tables beside the possibilities of techniques and algorithms that might be used, developed, or merged to create a high-performance recognition system. This guideline also could be useful for readers interested in this field by helping them extract the information from such papers easily and efficiently to reach the main structure along with the differences between the systems. In addition, this work recommends to researchers in this field to comprehend a specified categorical table in their work to provide readers with the main structure of their work that shows the proposed system’s structural layout and enables them to easily find the information and interests.

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Section: Syllabic Systemmentioning

confidence: 95%

“…In literature, lots of research can be found on handwritten CR in these scripts, for instance [11]- [13] work on Chinese, Japanese (Kanji), and Korean (Hanja), respectively. The accuracy rates for the scripts based on the aforementioned references are 99.39%, 99.64%, and 86.9%, respectively.…”

Section: Logographic Systemmentioning

confidence: 99%