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Portable and fast text detection

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Abstract

In this paper, we describe an efficient pipeline for real-time text detection to be implemented on different architectures, with particular reference to smart phones. The text detection pipeline is based on a rather standard segmentation followed by a classification of each segmented connected component. Segmentation is performed by a linear implementation of MSER, state-of-the-art for text detection, where we control the overall computational cost of the method by computing a set of descriptive features as segmentation goes on. Classification is carried out by a cascade of SVM classifiers, where each layer captures different levels of complexity by means of an appropriate choice of descriptive features and kernel functions. Each detected text element, or character, is finally merged into lines of text and words. Further on, each element can be fed to a multi-class classifier that performs character recognition—this functionality is currently under development. We report experiments aiming at assessing the appropriateness of the text detection procedure, in terms of both performance and speed, when running on both x86 and ARM processors.

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Notes

  1. http://www.dubout.ch/en/coding.html.

  2. Source code of the Text Segmentation, the libERtxt library, is available for download at https://bitbucket.org/slipguru.

  3. Source code of the general-purpose optimized classification library libMsC is available for download at https://bitbucket.org/slipguru.

  4. http://dag.cvc.uab.es/icdar2013competition.

  5. Dataset acquired for the project VIT—Vision for Innovative Transport—VII FP EU—SP4 Capacities Research for SMEs—n. 222199 http://www.vitproject.eu.

  6. http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/.

  7. https://developer.qualcomm.com/.

  8. http://www.csie.ntu.edu.tw/~cjlin/libsvm/.

  9. Eigen 3 http://eigen.tuxfamily.org.

  10. GLASSENSE is a regional project developed within the SI4Life Ligurian Regional Hub—Research and Innovation—Live Sciences http://www.si4life.com/.

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  1. L. Zini

    Present address: Imavis srl, Genova, Italy

Authors and Affiliations

  1. DIBRIS - Università degli Studi di Genova, Genova, Italy

    L. Zini & F. Odone

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  1. L. Zini
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  2. F. Odone
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Correspondence to F. Odone.

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Zini, L., Odone, F. Portable and fast text detection. Machine Vision and Applications 27, 845–859 (2016). https://doi.org/10.1007/s00138-016-0778-2

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