Zero Shot Learning Based Script Identification in the Wild The text recognition system for natural images or video frames containing multilingual text needs a method to first identify the written script and then recognize the word in the identified script. However, the occurrence of some scripts is rare as compared to others. Due to the availability of a few samples of the rare script, the supervised learning of the deep neural networks is difficult. To overcome this problem, we have proposed a zeroshot learning based method for script identification. We have also proposed architecture for script identification which fuses the global feature vector and the semantic embedding vector. The semantic embedding of the script is obtained by using the spatial dependency of the strokeu0027s sequence via the recurrent neural network. The proposed architecture shows superior results as compared to the baseline approaches.

Patch Aggregator for Scene Text Script Identification Script identification in the wild is of great importance in a multilingual robustreading system. The scripts deriving from the same language family share a large set of characters, which makes script identification a finegrained classification problem. Most existing methods make efforts to learn a single representation that combines the local features by making a weighted average or other clustering methods, which may reduce the discriminatory power of some important parts in each script for the interference of redundant features. In this paper, we present a novel module named Patch Aggregator (PA), which learns a more discriminative representation for script identification by taking into account the prediction scores of local patches. Specifically, we design a CNNbased method consisting of a standard CNN classifier and a PA module. Experiments demonstrate that the proposed PA module brings significant performance improvements over the baseline CNN model, achieving the stateoftheart results on three benchmark datasets for script identification: SIW13, CVSI 2015 and RRCMLT 2017.