Abstract: Provided are a ferritic stainless steel used for bipolar plates of fuel cells, a controlling method of surface roughness, a method of forming passivation films, and use. The ferritic stainless steel comprises C of less than or equal to 0.03 wt. %, N of less than or equal to 0.02 wt. %, Si of less than or equal to 0.4 wt. %, Mn of less than or equal to 0.5 wt. %, Cr of 16-23 wt. %, Cu of 0-2.0 wt. %, Mo of 1.8-2.5 wt. %, Ni of 0.2-2.0 wt. %, Ti of 0.1-0.5 wt. %, Nb of 0.005-0.5 wt. %, P of less than or equal to 0.02 wt. %, S of less than or equal to 0.02 wt. %, and a remainder composed of Fe and other unavoidable accompanying elements, and the ferritic stainless steel has a grain size number of 4-9. The ferritic stainless steel has excellent corrosion resistance and electrical conductivity, and good elongation and deformation as well, exhibiting both economy and cost advantages.

Abstract: The present invention discloses a method for recognizing a handwritten character, which includes the following steps of: obtaining a coarse classification template and a fine classification template; receiving a handwritten character input signal from a user, gathering a discrete coordinate sequence of trajectory points of the inputted character, and pre-processing the discrete coordinate sequence; extracting eigenvalues and calculating a multi-dimensional eigenvector of the inputted character; matching the inputted character with the coarse classification template to select a plurality of the most similar candidate character classes; and matching the eigen-transformed inputted character with sample centers of the candidate character classes selected from the fine classification template, and determining the most similar character classes among the candidate character classes. The present invention further discloses a system for recognizing a handwritten character.

Abstract: The present invention discloses a method for recognizing a handwritten character, which includes the following steps of: obtaining a coarse classification template and a fine classification template; receiving a handwritten character input signal from a user, gathering a discrete coordinate sequence of trajectory points of the inputted character, and pre-processing the discrete coordinate sequence; extracting eigenvalues and calculating a multi-dimensional eigenvector of the inputted character; matching the inputted character with the coarse classification template to select a plurality of the most similar candidate character classes; and matching the eigen-transformed inputted character with sample centers of the candidate character classes selected from the fine classification template, and determining the most similar character classes among the candidate character classes. The present invention further discloses a system for recognizing a handwritten character.