This paper describes an online handwritten Japanese character string recognition system integrating scores of geometric context, character recognition, and linguistic context. We give a string evaluation criterion for better integrating the multiple scores while overcoming the effect of string length variability. For measuring geometric context, we propose a statistical method for modeling both single- character and between-character plausibility. Our experimental results on TUAT HANDS databases show that the geometric context improves the character segmentation accuracy remarkably.