抄録
We developed an adaptive dictionary-selection method for recognizing low-quality characters in scene images. First, the environmental conditions of the input image are estimated using an initial dictionary. Then, a relevant dictionary from several dictionaries reflecting different environmental conditions is automatically selected based on the estimation and used for recognition. Experiments on characters in images of bookshelves showed that this method achieves a higher recognition rate (89.8%) than that using one fixed dictionary (76.4%). Furthermore, recognition accuracy improved from 89.8% to 95.2% when contextual postprocessing was used.
