Scene Text Segmentation Based on Local Image Phase Information and MSER Method

Diaz-Escobar, Julia; Kober, Vitaly

doi:10.1007/978-3-319-92198-3_21

Cited by 1 publication

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“…is paper is an extended version of the conference papers [32,33]. Unlike the previous works, we utilize the phase-based MSER approach and the AdaBoost classifier instead of applying only heuristic rules for the character filtering, retrieval, and grouping stages.…”

Section: Introductionmentioning

confidence: 99%

Natural Scene Text Detection and Segmentation Using Phase-Based Regions and Character Retrieval

Diaz-Escobar

Kober

2020

Mathematical Problems in Engineering

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Multioriented text detection and recognition in natural scene images are still challenges in the document analysis and computer vision communities. In particular, character segmentation plays an important role in the complete end-to-end recognition system performance. In this work, a robust multioriented text detection and segmentation method based on a biological visual system model is proposed. The proposed method exploits the local energy model instead of a common approach based on variations of local image pixel intensities. Features such as lines and edges are obtained by searching for the maximum local energy utilizing the scale-space monogenic signal framework. The candidate text components are extracted from maximally stable extremal regions of the local phase information of the image. The candidate regions are filtered by their phase congruency and classified as text and nontext components by the AdaBoost classifier. Finally, misclassified characters are restored, and all final characters are grouped into words. Experimental results show that the proposed text detection and segmentation method is invariant to scale and rotation changes and robust to perspective distortions, blurring, low resolution, and illumination variations (low contrast, high brightness, shadows, and nonuniform illumination). Besides, the proposed method achieves often a better performance compared with state-of-the-art methods on typical natural scene datasets.

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Section: Introductionmentioning

confidence: 99%