KR100255640B1 - Character recognizing method - Google Patents
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- KR100255640B1 KR100255640B1 KR1019970032912A KR19970032912A KR100255640B1 KR 100255640 B1 KR100255640 B1 KR 100255640B1 KR 1019970032912 A KR1019970032912 A KR 1019970032912A KR 19970032912 A KR19970032912 A KR 19970032912A KR 100255640 B1 KR100255640 B1 KR 100255640B1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/32—Digital ink
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/14—Image acquisition
- G06V30/142—Image acquisition using hand-held instruments; Constructional details of the instruments
- G06V30/1423—Image acquisition using hand-held instruments; Constructional details of the instruments the instrument generating sequences of position coordinates corresponding to handwriting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
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Abstract
Description
본 발명은 인식 방법에 관한 것으로서 보다 상세하게는 컴퓨터에 전자 펜으로 입력되는 문자의 인식 방법에 관한 것이다.The present invention relates to a recognition method, and more particularly, to a method for recognizing a character input to a computer by an electronic pen.
종래의 문자 인식은 한글을 초성, 중성, 종성으로 나누어 인식하는 자소 인식부(미도시)가 있고, 이들을 조합하여 최종 인식 결과를 나타내는 자소 조합부가 있다. 필기된 문자의 인식은 초성, 중성, 종성의 세 단계를 거친다. 먼저 초성을 인식하여 가능한 초성 부호를 c개로 결정하고 다음으로 c개의 각 초성에 대하여 나머지 부분을 중성으로 인식하여 각 초성에 대해 d개씩의 중성 후보를 낸다.Conventional character recognition includes a phoneme recognition unit (not shown) for recognizing Korean by dividing it into a consonant, a neutral, and a finality. Recognition of handwritten characters has three stages: primary, neutral, and final. First of all, the number of possible first codes is determined by recognizing the first consonants, and then, the remaining portions of the c initial consonants are recognized as neutral, and d neutral candidates are generated for each initial consonants.
마지막으로 각 중성 후보(총 c×d개)에 대해 종성 인식을 하여 각 중성에 대해 e개의 후보를 낸다. 이러한 결과를 가지고 초성, 중성, 종성의 결과를 조합하여 매칭점수를 계산하며 매칭점수가 낮을수록 잘 매칭된 것을 의미하여 최종 인식 결과로 나타낸다. 각 자소별 인식에서는 사용 가능한 모든 획의 범위 내에서 인식을 한다. 이와 같은 점수 계산 방법은 종래 기술로써 당업자에 의해 자명하므로 점수 계산 방법에 대한 상세한 내용의 설명은 생략한다.Finally, final recognition is made for each neutral candidate (total c × d), and e candidates are generated for each neutral. With this result, the matching score is calculated by combining the results of the initial, neutral, and finality. The lower the matching score, the better the match, and the final recognition result. Each phoneme recognition recognizes within the range of all available strokes. Since such a score calculation method is apparent to those skilled in the art as a related art, a detailed description of the score calculation method is omitted.
종래의 문자 인식 방법은 초성, 중성, 종성의 순서로 인식하므로 초성 후보의 개수를 많이 하면 뒤따르는 중성과 종성의 인식 횟수가 크게 증가되어 인식 속도가 떨어지는 단점이 있기 때문에 초성 후보의 개수와 중성 후보의 개수를 제한하여 인식 속도가 떨어지지 않도록 하고 있다. 그러나 초성 후보에 정답이 되는 문자가 없을 경우 인식하지 못하기 때문에 인식 성능이 저하되는 불편한 문제점이 있었다.The conventional character recognition method recognizes in the order of initial, neutral, and final, so that the number of initial candidates and neutral candidates is greatly reduced because the number of recognition of neutral and final trails is greatly increased. The number of times is limited so that the recognition speed does not fall. However, there is an uncomfortable problem that the recognition performance is lowered because the initial candidate is not recognized when there are no characters that are correct answers.
본 발명이 이루고자 하는 기술적인 과제는 인식된 결과를 보고 선별적으로 추가 인식하여 인식 성능을 높이는 문자 인식 방법을 제공하는 데 있다.The technical problem to be achieved by the present invention is to provide a character recognition method to increase the recognition performance by selectively recognized by looking at the recognized result.
도 1은 본 발명에 따른 문자 인식 방법을 보이는 흐름도 이다.1 is a flowchart illustrating a character recognition method according to the present invention.
본 발명이 이루고자 하는 기술적인 과제를 해결하기 위한 문자 인식 방법은 입력된 문자를 인식하는 방법에 있어서, 상기 입력된 문자를 구성하는 초성, 중성, 종성을 미리 설정된 일정 개수의 후보 자소로 인식하는 동시에 일정개(k)의 추가 자소를 추출하는 제1단계; 상기 인식된 문자의 매칭점수들 중 순위가 일등인 매칭점수가 제1임계치 보다 크거나, 총 후보 문자의 수가 제2임계치 보다 작은가를 판단하는 제2단계; 및 일등인 매칭점수가 제1임계치보다 크거나 또는 총후보 문자의 수가 제2임계치보다 작은 경우에는 상기 제1단계에서 추출한 추가자소에 의해 문자인식을 다시 수행하는 제3단계를 포함하는 것이 바람직하다.The character recognition method for solving the technical problem to be achieved by the present invention, in the method for recognizing the input character, while recognizing the initial, neutral, and final constituting the input character as a predetermined number of candidate phonemes A first step of extracting a certain number of additional phonemes; A second step of determining whether a matching score having a first rank among the recognized scores of the recognized characters is greater than a first threshold value or the total number of candidate characters is less than a second threshold value; And a third step of performing character recognition by the additional elements extracted in the first step when the first matching score is larger than the first threshold value or the total number of candidate letters is smaller than the second threshold value. .
이하, 첨부된 도면을 참조하여 본 발명을 상세히 설명한다.Hereinafter, with reference to the accompanying drawings will be described in detail the present invention.
도 1은 본 발명에 따른 문자 인식 방법을 보이는 흐름도 이다.1 is a flowchart illustrating a character recognition method according to the present invention.
도 1에 도시된 흐름도는 문자 필체를 입력하는 단계(100), 초성에 따른 후보 자소와 추가 자소를 소정의 개수로 인식하고 점수를 계산하는 단계(110), 초성의 후보 자소에 따른 중성 자소를 인식하고 점수를 계산하는 단계(120), 중성 자소에 따른 종성 자소를 인식하고 점수를 계산하는 단계(130), 종성까지의 자소를 조합하고 최종 점수를 계산하는 단계(140), 조합한 자소의 1등 유사도 점수가 나쁘거나 총 후보 문자의 수가 적은가를 판단하는 단계(150), 초성의 추가 자소에 대한 중성의 자소를 인식하고 점수를 계산하는 단계(160), 중성 자소에 따른 종성 자소를 인식하고 점수를 계산하는 단계(170), 종성까지의 자소를 조합하고 최종 점수를 계산하는 단계(180), 입력된 자소 조합을 점수순으로 정렬하는 단계(190)로 구성된다.The flowchart shown in FIG. 1 includes the steps of inputting a character handwriting (100), recognizing candidates and additional phonemes according to the initial number as a predetermined number, and calculating a score (110), and neutral phonemes according to the first candidate phonemes Recognizing and calculating the
이어서, 도1에 도시된 흐름도를 설명하면 다음과 같다.Next, the flowchart shown in FIG. 1 will be described.
본 발명의 문자 인식 방법은 전자 펜을 사용하는 모든 장치의 문자 입력 방법으로 사용할 수 있다. 또한 범용 컴퓨터에서의 키보드를 대신한 입력이나 개인휴대 단말기(PDA : Personal Digital Assistant)와 같은 크기가 작은 휴대용 장치에서 사용하는 전자 펜을 통한 문자 입력에 사용할 수 있다.The character recognition method of the present invention can be used as a character input method of all devices using the electronic pen. In addition, it can be used for text input through an electronic pen used in a small portable device such as a personal digital assistant (PDA) or an alternative to a keyboard in a general-purpose computer.
본 발명은 종래의 인식 방법에서 각 초성, 중성, 종성 후보를 k개씩 추가 출력하는 부분(미도시)과 종래의 인식 방법에 의해 나온 인식 결과의 매칭 점수와 후보 문자의 수를 판단하여 추가 인식을 할 것인지를 결정하는 부분(미도시)이 구비되어 있다. 따라서 추가 인식을 해야 하는 조건이 만족되었을 경우 이미 인식한 것 외에 추가된 각 후보 자소 k개에 대한 추가 인식을 하여 최종 인식 결과를 나타낸다.According to the present invention, additional recognition is performed by judging the number of candidate characters and the matching score of the recognition result obtained by the conventional recognition method and the portion for additionally outputting k initial candidates, the neutral candidates, and the candidate candidates. A portion (not shown) for determining whether or not to be provided is provided. Therefore, when the conditions for further recognition are satisfied, the final recognition result is shown by performing additional recognition on each of the candidate k elements added in addition to the already recognized ones.
사용자는 문자 필체를 입력한다(100단계). 입력된 필체중 초성에 따른 후보 자소 c개와 추가 자소 k개를 인식하고 점수를 계산한다(110단계). 입력된 필체와 후보 자소를 비교하여 점수를 계산하는데, 점수가 낮을수록 잘 매칭된 것을 의미한다. 각 자소별 후보 자소는 저장된 자소와 입력하는 자소의 시작점과 마침점을 비교하여 저장된 자소와 입력하는 자소의 시작점과 마침점이 동일한 자소를 후보 자소로 정한다. 각 자소별 인식에서 후보 자소를 추가로 더 내는 것은 속도에 큰 영향이 없으므로 후보를 더 많이 내어도 무방하다.The user enters the character handwriting (step 100). Recognizing c candidate phonemes and k additional phonemes according to the initial consonants of the handwriting, the score is calculated (step 110). The score is calculated by comparing the input handwriting with the candidate phoneme, and the lower the score, the better the match. The candidate phoneme for each phoneme compares the stored phoneme with the starting point and the end point of the inputted phoneme to determine the candidate phoneme having the same starting point and the end point of the stored phoneme and the inputted phoneme. In the case of recognition of each phoneme, the additional candidate phonemes have no significant effect on speed, so more candidates may be offered.
초성의 후보 자소 c개에 따른 중성 자소 d개를 인식하고 점수를 계산한다(120단계). 중성 자소 d개에 따른 종성 자소 e개를 인식하고 점수를 계산한다(130단계).Recognizing the number of neutral neuminal d according to c candidate phonemes of the initial number is calculated (step 120). Recognize the number of final phonemes according to the number of neutral vowels and calculate the score (step 130).
종성 자소 까지 인식한 후에 자소를 조합 즉, 문자를 인식하고 최종 점수를 계산한다(140단계). 초성, 중성, 종성의 입력에 의해 할당된 점수를 합산하여 최종 점수를 계산한다.After the final phoneme is recognized, the phoneme is combined, that is, the letters are recognized and the final score is calculated (step 140). The final score is calculated by summing up the scores assigned by inputs of primary, neutral, and final.
최종 점수는 각 자소별 점수를 합산하여 조합한 자소의 1등 유사도 점수가 나쁘거나 총 후보 문자의 수가 적은가를 판단한다(150단계). 여기서 1등 유사도 점수는 각 자소별 점수를 합산하여 조합한 점수 중 매칭 점수가 가장 낮은 점수를 의미하며, 1등 유사도 점수와 임계치를 비교하여 1등 유사도 점수가 임계치보다 큰 경우 1등 유사도 점수가 나쁘다고 표현하였다. 여기서 임계치는 일종의 기준 매칭 점수로 이미 설정된 값을 나타낸다. 총 후보 문자수 역시 임계치와 비교하는데, 여기서 임계치는 기준 후보 문자수로 이미 설정된 값을 나타낸다.The final score determines whether the first similarity score of the phonemes combined by adding the scores of each phoneme is bad or the total number of candidate letters is small (step 150). Here, the first similarity score means the lowest matching score among the scores of the sum of the scores of each phoneme, and when the first similarity score is greater than the threshold, the first similarity score is displayed. Expressed bad. Here, the threshold represents a value already set as a kind of reference matching score. The total number of candidate characters is also compared with the threshold, where the threshold represents a value already set as the reference candidate number of characters.
조합한 자소의 1등 유사도 점수가 나쁘거나 총 후보 문자의 수가 적지 않으면 자소 조합을 점수순으로 정렬한다(190단계).If the first similarity score of the combined phonemes is bad or the total number of candidate letters is not small, the phoneme combinations are sorted in order of score (step 190).
조합한 자소의 1등 유사도 점수가 나쁘거나 총 후보 문자의 수가 적으면 초성의 추가 자소 k개에 대한 중성의 자소를 인식하여 점수를 계산한다(160단계). 초성의 추가 자소 k개에 대한 중성 자소에 따른 종성 자소를 인식하고 점수를 계산한다(170단계). 종성 자소 까지 인식한 후에 자소를 조합 즉, 문자를 인식하고 최종 점수를 계산한다(180단계).If the first similarity score of the combined phonemes is bad or the total number of candidate letters is small, the score is recognized by recognizing the neutral phonemes for k additional phonemes of the initial character (step 160). Recognize the final phoneme according to the neutral phoneme for k additional phonemes and calculate the score (step 170). After the final phoneme is recognized, the phoneme is combined, that is, the letters are recognized and the final score is calculated (step 180).
조합된 자소를 점수순으로 정렬한다(190단계). 조합된 자소를 점수순으로 정렬할 때에는 후보 문자에 의해 조합된 자소에 대한 점수와 추가 문자에 의해 조합된 자소에 대한 점수를 비교하여 낮은 순서대로 정렬한다.The combined phonemes are sorted by score (step 190). When sorting the combined phonemes in the score order, the scores for the phonemes combined by the candidate letters and the scores for the phonemes combined by the additional letters are compared and sorted in the lower order.
이와 같이 하여 초성 후보의 개수를 c개로 제한하므로 써 입력된 필체인 초성이 c개 내에 들지 않게 되었을 경우에 발생할 수 있는 오 인식을 줄여 준다. 이러한 추가 인식의 경우 제대로 인식되었을 경우에는 실행하지 않도록 하여 인식 속도가 느려지는 문제도 동시에 해결할 수 있다.In this way, the number of initial candidates is limited to c, thereby reducing the false recognition that can occur when the input type of the written initials is not within c. In case of such additional recognition, if it is properly recognized, the problem of slowing down the recognition speed can be solved at the same time.
상술한 바와 같이 본 발명에 따르면, 인식된 결과를 보고 선별적으로 추가 인식하여 인식 성능을 높일 수 있는 효과가 있다.As described above, according to the present invention, the recognition result may be selectively recognized to increase the recognition performance.
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