An effective extraction algorithm for character recognition in scene images is developed. Character candidate regions are detected by an image-segmentation method based on adaptive thresholding and by evaluating gray-level difference between adjacent regions. Character pattern candidates are obtained by associating the detected regions according to their positions and gray levels and evaluating the aspect ratio of the rectangle that circumscribes the associated regions. The character-recognition process selects character pattern candidates with high similarities to any categories in a dictionary (high similarity patterns). Highly promising experimental results have been obtained using the method on 100 images acquired under uncontrolled lighting. This algorithm can be applied to read characters of any size and format under various lighting conditions.