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.
['Ali Abedi', 'Ehsanollah Kabir']
Text image super resolution using within-scale repetition of characters and strokes