Mask R-CNN End-to-End Text Detection and Recognition

• Published in: 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) pp. 1787 - 1793
• Main Authors: Shivajirao, Sandeep, Hantach, Rim, Ben Abbes, Sarra, Calvez, Philippe
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2019
• Subjects: Cascade Region Proposal Network; Computational modeling; Convolutional Neural Networks; Feature extraction; Image recognition; Image segmentation; Mask Text Recognition; Neural networks; Proposals; Scene Text Recognition; Text recognition
• DOI: 10.1109/ICMLA.2019.00289

Text detection and recognition have witnessed drastic improvements in the field of computer vision. This end-toend model comprising of the detection and recognition models scales to provide higher accuracy. The most important phase in this end-to-end approach is the detection phase, as it plays an important role to identify the text. To address this issue, different approaches have been proposed. However, most of the methods produce lower efficiency to detect and recognize real world text. In this paper, we propose a new approach to investigate the challenges that the existing models possess and improve the efficiency of the detection and in turn increases the accuracy of text recognition. The proposed method outperforms the state-ofthe-art approaches due to the use of deblurring and sharpening to reduce noise in the pre-processing stage, followed by the cascade region proposal network model to improve the detection of real world text using non max suppression. Experimentations on real word datasets highlight the effectiveness of our method.