A Korean character is made up of sequential additions of basic alphabets under positioning rules, thus there are a large amount of characters and consequently there exist many similar patterns. Accordingly, it is an important problem to avoid the ambiguities among the similar characters.
Recently a syntactic method which extracts basic alphabets sequentially according to positioning rules under the control of tree grammars is applied, but the ambiguity problem remains, and it has been doubtful whether the designed grammar is effective in case of hand-written characters.
In this thesis, a recognition system of Korean characters composing of two main bodies, description and structural analysis, is suggested in order to minimize the ambiguities.
Detection of a number of nodes over the input pattern and codification of strokes between nodes by some predetermined primitives is presented in order to describe the input character as a bi-directional graph.
The structural analysis contains two procedures, one is top-down nondeterministic parsing and the other is bottom-up segmentation process to resolve the fatal ambiguity problems occurring from the concatenations between basic alphabets.
Very high recognition ratio has obtained through the experimentation by digital computer, and the author have made sure that this system is powerful for recognizing Korean characters.
지금까지 한글 문자의 자동 인식에 관한 연구는 영어와 같은 다른 문자에 비하면 그 양이나 내용에 있어 극히 미미한 것이었다.
이것은 한글의 한문자가 기본 자모의 구조적 결합으로 이루어지기 때문에 그 문자의 수효가 방대하고 따라서 비슷한 문자들이 너무나 많다는 사실에 연유한다.
최근 tree grammar의 control하에 positioning rule에 따라서 순차적으로 기본 자모를 추출해내는 syntactic한 방법이 소개되었지만 이것으로는 비슷한 문자들 사이의 ambiguity 문제를 해결할 수 없었고 인위적으로 design된 grammar가 필기체의 경우에 어느정도 효과적인지가 의문시되어 왔다.
본 논문에서는 ambiguity 문제를 최소한으로 줄이고 필기체의 경우에도 적합한 grammar를 갖는 인식 system이 제시되었다.
실험결과 91%의 좋은 인식률을 보였다.