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Text image super resolution using within-scale repetition of characters and strokes

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Abstract

In text images, there are some frequently used characters repeating more than others. Likewise, some characters have common strokes. This characteristic is used in this paper for machine-printed text-image super resolution. After segmenting the input low-resolution image into text lines and characters, 1) the characters are clustered and the clusters with large number of members, corresponding to the frequent characters, are detected. 2) A text-specific multiple-image super resolution is applied to the members of each large cluster and the result is verified by the recognition confidence of an OCR system. 3) A training example set is then constructed by extracting patches from the low-resolution frequent characters and their verified super resolution. Using this example set, infrequent characters are super resolved through the neighbor embedding SR algorithm. By placing all the super-resolved characters on their corresponding positions in the high-resolution grid, the final high-resolution image is generated. Our method achieves significant improvements in visual image quality and OCR character accuracy compared to related SR methods.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

    Ali Abedi & Ehsanollah Kabir

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  1. Ali Abedi
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  2. Ehsanollah Kabir
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Correspondence to Ali Abedi.

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Abedi, A., Kabir, E. Text image super resolution using within-scale repetition of characters and strokes. Multimed Tools Appl 76, 16415–16438 (2017). https://doi.org/10.1007/s11042-016-3919-8

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  • DOI: https://doi.org/10.1007/s11042-016-3919-8

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