TeLCoS: OnDevice Text Localization with Clustering of Script

TL;DR

TeLCoS is a lightweight, real-time on-device text localization and script clustering method that reduces overhead by integrating script identification into the localization process, suitable for resource-constrained environments.

Contribution

It introduces a dual-branch CNN that performs text localization and script clustering simultaneously, with a novel channel pruning mechanism for efficiency.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Runs in 60 ms on Exynos 990 chipset device.

Uses only 1.15 million parameters for the network.

Abstract

Recent research in the field of text localization in a resource constrained environment has made extensive use of deep neural networks. Scene text localization and recognition on low-memory mobile devices have a wide range of applications including content extraction, image categorization and keyword based image search. For text recognition of multi-lingual localized text, the OCR systems require prior knowledge of the script of each text instance. This leads to word script identification being an essential step for text recognition. Most existing methods treat text localization, script identification and text recognition as three separate tasks. This makes script identification an overhead in the recognition pipeline. To reduce this overhead, we propose TeLCoS: OnDevice Text Localization with Clustering of Script, a multi-task dual branch lightweight CNN network that performs real-time on device Text Localization and High-level Script Clustering simultaneously. The network drastically reduces the number of calls to a separate script identification module, by grouping and identifying some majorly used scripts through a single feed-forward pass over the localization network. We also introduce a novel structural similarity based channel pruning mechanism to build an efficient network with only 1.15M parameters. Experiments on benchmark datasets suggest that our method achieves state-of-the-art performance, with execution latency of 60 ms for the entire pipeline on the Exynos 990 chipset device.