In our daily life, we are spending hours for reading characters/documents. This is because "reading" is a primal mean of acquiring information. "Reading-life log" is a field of research that extracts fruitful information for enriching our life by analyzing pairs of reading activity and characters/documents read by persons. In the case of documents, we can estimate many things from how much we read (wordometer, reading detection) to how well we understand (the level of understanding and

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... cy) by analyzing eye gaze obtained by eyetrackers. For characters in scenes, we can estimate what a person is doing by knowing characters read by him/ her. In addition, a camera mounted on a finger enables us to recognize scene characters that are closely related to the activity and thus can be used to help the person. Fundamental technologies which support reading-life log are recognition and retrieval of external environment inputted as images. In order to process a huge amount of data for recognition and retrieval, we apply approximate nearest neighbor search. In this article, I describe the fundamental technologies and their application to reading-life log.