H‐WordNet: a holistic convolutional neural network approach for handwritten word recognition. Issue 9 (21st May 2020)

• Record Type: Journal Article
• Authors: Das, Dibyasundar; Nayak, Deepak Ranjan; Dash, Ratnakar; Majhi, Banshidhar; Zhang, Yu‐Dong
• Is Part Of: IET image processing. Volume 14:Issue 9(2020)
• Page Start: 1794
• Page End: 1805
• Publication Date: 2020-05-21
• Subjects: natural language processing -- feature extraction -- learning (artificial intelligence) -- image segmentation -- handwriting recognition -- handwritten character recognition -- image classification -- convolutional neural nets; handwritten word recognition -- isolated characters -- cursiveness -- complex shapes -- compound characters -- recognition task -- character‐level segmentation -- deep convolutional neural network‐based holistic method -- H‐WordNet model -- convolutional layers -- standard handwritten Bangla word database -- Bangla word images -- atomic character classes; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1398
• Languages: English
• ISSNs: 1751-9659

Abstract

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.