Development of Vertical Text Interpreter for Natural Scene Images
Open Access
- 19 October 2021
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Access
- Vol. 9, 144341-144351
- https://doi.org/10.1109/access.2021.3121608
Abstract
Automatic text recognition in natural scene images is essential for accessing information and understanding our surroundings. Scene text orientations include horizontal scene texts, arbitrarily oriented scene texts, curved scene texts, and vertically oriented scene texts. While attention has been given to horizontal, arbitrarily oriented, and curved text, limited research has been carried out on vertically oriented scene text recognition. To this end, we propose Vertical Text Interpreter, an autonomous vertically oriented scene text recognizer model. Vertical Text Interpreter detects and recognizes vertically oriented scene texts in natural scenes, including vertically-stacked texts, bottom-to-top vertical texts, and top-to-bottom vertical texts. It consists of a shared convolutional neural network, a Vertical Text Spotter, and a Vertical Text Reader. We developed a dataset, namely Vertically Oriented Scene Text 1250 Dataset, created as part of this research, addressing the need for a dataset for this category of scene texts. The performance of the Vertical Text Interpreter is evaluated using benchmark datasets and the VOST-1250 dataset. Results show that Vertical Text Interpreter can detect and recognize different types of vertically oriented scene texts simultaneously. For future work, we can explore Vertical Text Interpreter for the contexts such as reading assistance and visual navigation systems.Funding Information
- Swinburne Melbourne Sarawak Research Collaboration Scheme
This publication has 26 references indexed in Scilit:
- Date-field retrieval in scene image and video frames using text enhancement and shape codingNeurocomputing, 2018
- Review on Text Recognition in Natural Scene ImagesPublished by Springer Science and Business Media LLC ,2017
- Cascaded Segmentation-Detection Networks for Word-Level Text SpottingNinth International Conference on Document Analysis and Recognition (ICDAR 2007), 2017
- Improving patch-based scene text script identification with ensembles of conjoined networksPattern Recognition, 2017
- Severity Prediction of Traffic Accidents with Recurrent Neural NetworksApplied Sciences, 2017
- TextCatcher: a method to detect curved and challenging text in natural scenesInternational Journal on Document Analysis and Recognition (IJDAR), 2016
- ICDAR 2015 competition on Robust ReadingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Fully convolutional networks for semantic segmentationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Rich Feature Hierarchies for Accurate Object Detection and Semantic SegmentationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- ICDAR 2013 Robust Reading CompetitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2013