2. The identifying method of hand-written Latin letter, as recited in claim 1 , wherein step (b) comprises the following steps of: (b.1) smoothly filtering said chirography coordinates stored in the structural array, removing hardware noise, and eliminating burr and broken line formed in writing process; (b.2) linearly and nonlinearly normalizing said chirography coordinate point lattice in the matrix format transformed from said chirography coordinates processed in step (b.1) by using linear normalization and non-linear normalization algorithm of on-line single-point width hand-written style to obtain new chirography coordinate point lattice; and (b.3) adding points to said new chirography coordinate point lattice processed by step (b.2) to prevent from loosing effective characteristic point.
2. The identifying method of hand-written Latin letter, as recited in claim 1 , wherein step (b) comprises the following steps of: (b.1) smoothly filtering said chirography coordinates stored in the structural array, removing hardware noise, and eliminating burr and broken line formed in writing process; (b.2) linearly and nonlinearly normalizing said chirography coordinate point lattice in the matrix format transformed from said chirography coordinates processed in step (b.1) by using linear normalization and non-linear normalization algorithm of on-line single-point width hand-written style to obtain new chirography coordinate point lattice; and (b.3) adding points to said new chirography coordinate point lattice processed by step (b.2) to prevent from loosing effective characteristic point. 3. The identifying method of hand-written Latin letter, as recited in claim 2 , wherein step (b.2) comprises steps of: (b.2.1) linearly normalizing said chirography coordinate point lattice in the matrix format transformed from said chirography coordinates processed in step (b.1) to a standard coordinate point lattice by adopting coordinates frame scaling and direct projection method; (b.2.2) expressing standard coordinate point of standard coordinate point lattice as (x,y), and classifying said standard coordinate point (x,y) into a chirography coordinate point and a non-chirography coordinate point; (b.2.3) calculating a density function d(x,y) of said standard coordinate point (x,y), d ⁡ ( x , y ) = { Max ⁢ ( A / Lx , A / Ly ) , Lx + Ly ⁢ ⁢ π ⁢ ⁢ 6 ⁢ ⁢ A 0 , Lx + Ly ≥ 6 ⁢ ⁢ A , wherein, Lx is a transverse density of a current standard coordinate point, Ly is a vertical density of a current standard coordinate point, A is a length of said standard coordinate point lattice, and a value of A is 64; (b.2.4) calculating a level density projection function H(x) and a vertical density projection function V(y) of said standard coordinate point (x,y) according to said density function d(x,y) of said standard coordinate point (x,y), H ⁡ ( x ) = ∑ y = 1 J ⁢ [ d ⁡ ( x , y ) + a H ] , ⁢ V ⁡ ( y ) = ∑ x = 1 I ⁢ [ d ⁡ ( x , y ) + a v ] , wherein, d(x,y) is said density function of said standard coordinate point (x,y), I×J is a dimension of said standard coordinate point lattice, a value of I×J is 64×64, a H and a v are correction coefficient, and both value of a H and a v are 0.1; and (b.2.5) non-linearly normalizing said chirography coordinate point lattice by using a level density projection function H(x) and a vertical density projection function V(y) to obtain a new chirography coordinate point lattice, m = ∑ x = 1 i ⁢ H ⁡ ( x ) × M ∑ x = 1 I ⁢ H ⁡ ( x ) , ⁢ n = ∑ y = 1 j ⁢ V ⁡ ( y ) × N ∑ y = 1 J ⁢ V ⁡ ( y ) , wherein I×J is the dimension of said standard coordinate point lattice, the value of I×J is 64×64; M×N is a dimension of said new chirography coordinate point lattice processed by non-linear normalization algorithm, a predetermined value of M×N is 64×64; (m,n) is a coordinate point of said new chirography coordinate point lattice; i=1,2, . . . , I , j=1,2, . . . , J.