Real-Time Localization of Multi-Oriented Text in Natural Scene Images

Identificador:  TDX:3140

Handle: https://hdl.handle.net/20.500.11797/TDX3140

Autores:  Gironés Sancho, Xavier

Resumen:
This thesis focuses on the problem of text localization in natural scene images from the perspective of time-efficiency. Towards this end, a multi-oriented text localization method in natural images suitable for real-time processing of high-definition video on portable and mobile devices is introduced. The proposed method is based on the connected component (CC) approach: First, CCs are isolated by convolving a multi-scale pyramid with a specifically designed linear spatial filter, followed by hysteresis thresholding. Next, non-textual CCs are pruned employing a cascade of local classifiers fed with increasingly extended feature vectors, where the stroke width feature is estimated in linear time complexity by computing the maximal inscribed squares in the CCs. Candidate CCs and their neighbors are subsequently checked with a context-aware classifier that takes into account the target CCs and their vicinity. Lastly, text sequences are extracted in all pyramid levels and fused using dynamic programming. The proposed method is capable of processing 1080p HD video at nearly 30 frames per second on a standard laptop without requiring a GPU. Furthermore, when benchmarked on the ICDAR 2013 Robust Reading and on the ICDAR 2015 Incidental Scene Text datasets, it performed more than twice faster than the state-of-the-art, while still delivering competitive results in terms of precision and recall. Additionally, this thesis introduces a new family or rational approximations of the arctangent function valid in the [0, π/2] range that can be easily extended to two and four quadrants, and a new technique for vehicle license plate localization in unconstrained environments is presented as a practical use case leveraging the text localization system described in this research.