5. The method of claim 1 wherein the one or more first metrics include one or more differences ΔPQ i in character-to-character distance distributions between the distance distribution of the distances between a particular input character c i and all input characters c k in the input-character set, and wherein the one or more second metrics include one or more differences ΔPQ i in character-to-character distance distributions between the distance distribution of the distances between a particular output character ĉ i corresponding to input character c i and all input characters c k in the input-character set.
5. The method of claim 1 wherein the one or more first metrics include one or more differences ΔPQ i in character-to-character distance distributions between the distance distribution of the distances between a particular input character c i and all input characters c k in the input-character set, and wherein the one or more second metrics include one or more differences ΔPQ i in character-to-character distance distributions between the distance distribution of the distances between a particular output character ĉ i corresponding to input character c i and all input characters c k in the input-character set. 7. The method of claim 5 wherein each ΔPQ i is generated according to: P ik = A ⁢ ⁢ ⅇ - distance ⁡ ( input i , input k ) Q ik = B ⁢ ⁢ ⅇ - distance ⁡ ( output i , input k ) Δ ⁢ ⁢ PQ i = ∑ k - 1 N ⁢ P ik ⁡ ( log 2 ⁡ ( P ik Q ik ) ) wherein distance(input i , input k ) is the distance between input character c i and input character c k ; distance (output i , input k ) is the distance between output character ĉ i and input character c k ; and A and B are constants.