Character recognition in bookshelf images using context-based image templates

This paper proposes a method for recognizing degraded characters in bookshelf images captured by a digital camera. We adopt displacement matching and templates that include neighboring characters or parts thereof to cope with the degradation. The templates are referred as context-based image templates, since they offer more contextual information than single-letter templates. Such templates are effective wherever there is a restricted word set, such as journal titles, year, month, volume, and number. Experiments with 3,468 characters in nine bookshelf images show that this method achieves a higher recognition rare (96.3%) than single-letter templates (88.4%).