Open AccessH‐WordNet: a holistic convolutional neural network approach for handwritten word recognition
Author(s) -
Das Dibyasundar,
Nayak Deepak Ranjan,
Dash Ratnakar,
Majhi Banshidhar,
Zhang YuDong
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.1398
Subject(s) - wordnet , computer science , artificial intelligence , convolutional neural network , pattern recognition (psychology) , word (group theory) , feature extraction , natural language processing , bengali , intelligent word recognition , pooling , speech recognition , feature (linguistics) , character (mathematics) , handwriting recognition , scripting language , segmentation , intelligent character recognition , image (mathematics) , character recognition , mathematics , geometry , linguistics , philosophy , operating system
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.