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Text detection in the wild is a well-known problem that becomes morechallenging while handling multiple scripts. In the last decade, some scriptshave gained the attention of the research community and achieved good detectionperformance. However, many scripts are low-resourced for training deeplearning-based scene text detectors. It raises a critical question: Is there aneed for separate training for new scripts? It is an unexplored query in thefield of scene text detection. This paper acknowledges this problem andproposes a solution to detect scripts not present during training. In thiswork, the analysis has been performed to understand cross-script textdetection, i.e., trained on one and tested on another. We found that theidentical nature of text annotation (word-level/line-level) is crucial forbetter cross-script text detection. The different nature of text annotationbetween scripts degrades cross-script text detection performance. Additionally,for unseen script detection, the proposed solution utilizes vector embedding tomap the stroke information of text corresponding to the script category. Theproposed method is validated with a well-known multi-lingual scene text datasetunder a zero-shot setting. The results show the potential of the proposedmethod for unseen script detection in natural images.

Separate Scene Text Detector for Unseen Scripts is Not All You Need