Indic Handwritten Script Identification using Offline-Online Multimodal Deep Network
Ayan Kumar Bhunia, Subham Mukherjee, Aneeshan Sain, Ankan Kumar, Bhunia, Partha Pratim Roy, Umapada Pal

TL;DR
This paper introduces a multimodal deep network for Indic script identification that uses both offline and online data modalities, trained on character-level data, achieving state-of-the-art results with a unified offline-online framework.
Contribution
The paper presents a novel multimodal deep network with a conditional fusion scheme for script identification, trained on character-level data, enabling simultaneous offline-online identification.
Findings
Outperforms traditional classifiers and deep learning methods.
Achieves state-of-the-art performance with character-level training.
Works effectively for multiple Indic scripts and English.
Abstract
In this paper, we propose a novel approach of word-level Indic script identification using only character-level data in training stage. The advantages of using character level data for training have been outlined in section I. Our method uses a multimodal deep network which takes both offline and online modality of the data as input in order to explore the information from both the modalities jointly for script identification task. We take handwritten data in either modality as input and the opposite modality is generated through intermodality conversion. Thereafter, we feed this offline-online modality pair to our network. Hence, along with the advantage of utilizing information from both the modalities, it can work as a single framework for both offline and online script identification simultaneously which alleviates the need for designing two separate script identification modules…
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