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

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.