Preprint Article Version 1 This version is not peer-reviewed

Simultaneous Recognition of Horizontal and Vertical Text in Natural Images

Version 1 : Received: 4 December 2018 / Approved: 11 December 2018 / Online: 11 December 2018 (07:24:04 CET)

How to cite: Choi, C.; Yoon, Y.; Lee, J.; Kim, J. Simultaneous Recognition of Horizontal and Vertical Text in Natural Images. Preprints 2018, 2018120114 (doi: 10.20944/preprints201812.0114.v1). Choi, C.; Yoon, Y.; Lee, J.; Kim, J. Simultaneous Recognition of Horizontal and Vertical Text in Natural Images. Preprints 2018, 2018120114 (doi: 10.20944/preprints201812.0114.v1).

Abstract

Recent state-of-the-art scene text recognition methods have primarily focused on horizontal text in images. However, in several Asian countries, including China, large amounts of text in signs, books, and TV commercials are vertically directed. Because the horizontal and vertical texts exhibit different characteristics, developing an algorithm that can simultaneously recognize both types of text in real environments is necessary. To address this problem, we adopted the direction encoding mask (DEM) and selective attention network (SAN) methods based on supervised learning. DEM contains directional information to compensate in cases that lack text direction; therefore, our network is trained using this information to handle the vertical text. The SAN method is designed to work individually for both types of text. To train the network to recognize both types of text and to evaluate the effectiveness of the designed model, we prepared a new synthetic vertical text dataset and collected an actual vertical text dataset (VTD142) from the Web. Using these datasets, we proved that our proposed model can accurately recognize both vertical and horizontal text and can achieve state-of-the-art results in experiments using benchmark datasets, including the street view test (SVT), IIIT-5k, and ICDAR. Although our model is relatively simple as compared to its predecessors, it maintains the accuracy and is trained in an end-to-end manner.

Subject Areas

directional encoding mask; selective attention network; supervised learning; horizontal and vertical text recognition

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.