NDLI: H-WordNet: a holistic convolutional neural network approach for handwritten word recognition

Content Provider	IET Digital Library
Author	Das, Dibyasundar Nayak, Deepak Ranjan Dash, Ratnakar Majhi, Banshidhar Zhang, Yu Dong
Abstract	Segmentation of handwritten words into isolated characters and their recognition are challenging due to the presence of high variability and cursiveness in Indian scripts. The complex shapes and availability of numerous atomic character classes, compound characters, modifiers, ascendants, and descendants make the recognition task even more difficult. A holistic approach effectively tackles such issues by avoiding the character-level segmentation and the earlier holistic methods have been mostly developed using multi-stage machine learning architecture. In this study, a deep convolutional neural network-based holistic method termed ‘H-WordNet’ is proposed for handwritten word recognition. The H-WordNet model includes merely four convolutional layers and one fully connected layer to effectively classify the word images', which lead to a significant reduction in parameters. The efficacy of different pooling operations with the proposed model is investigated. The main purpose of this study is to avoid the need for handcrafted feature extraction and obtain a more stable and generalised system for word recognition. The proposed model is evaluated using a standard handwritten Bangla word database (CMATERdb2.1.2), which contains 18000 Bangla word images of 120 different categories and it obtained a higher recognition accuracy of 96.17% when compared to recent state-of-the-art methods.
Starting Page	1794
Ending Page	1805
Page Count	12
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 9, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1398
Journal	IET Image Processing
Publisher Date	2020-03-25
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Atomic Character Classes Bangla Word Image Character-level Segmentation Complex Shapes Compound Characters Computer Vision And Image Processing Technique Convolutional Layer Convolutional Neural Nets Cursiveness Deep Convolutional Neural Network-based Holistic Method Feature Extraction H-WordNet Model Handwriting Recognition Handwritten Character Recognition Handwritten Word Recognition Image Classification Image Recognition Image Segmentation Isolated Characters Knowledge Engineering Technique Learning in AI Natural Language Processing Neural Computing Technique Recognition Task Standard Handwritten Bangla Word Database
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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