Character recognition in bookshelf images by automatic template selection

This paper presents a multiple-dictionary method for recognizing low-quality characters in scene images. First, the environmental conditions of an input image are estimated using an initial dictionary. Then, a relevant dictionary from multiple dictionaries reflecting different environmental conditions is automatically selected from the estimation and used for recognition. Experiments are made for characters in images of bookshelves. The results show that the proposed method achieves a higher recognition rate (89.8%) than that obtained by using a single dictionary (76.4%). Furthermore, recognition accuracy improves from 89.8% to 95.2% using contextual postprocessing.