Automated analysis of mixed documents consisting of printed Korean/alphanumeric texts and graphic images

Analysis of Mixed Korean Documents Using the Branch and Bound Algorithm Based on DP Matching

“…We use them in both character classification and recognition steps. In conventional recognition algorithms for printed characters, they are computed at some specific positions [24,25]. Note that in handwritten character recognition, the feature value detected at fixed position may vary depending on writing style, so in this paper, by averaging the detected feature values over the specific area, we can build a recognizer that successfully copes with various shapes of characters.…”

“…We use a hierarchical recognition algorithm for alphanumeric characters [9,10]. That is, characters are classified first by the number of color changes, distance, and projection values.…”

“…Although this algorithm can be constructed and employed for recognition of alphanumeric characters, it is difficult to di-rectly apply the algorithm to recognition of Korean characters in which more than 11,000 characters consisting of different types of phonemes are possible. The structural complexity and diverse font variations of Korean characters have been topics of research [4,5], and various recognition techniques of Korean characters have been proposed in recent decades [6][7][8][9][10]. The handwritten Korean character recognition method robust to distortion was proposed by using adaptive nonlinear shape matching [11].…”

“…Also algorithms based on neural network [6,7] have been proposed; however, these algorithms were focused on recognition of Korean characters, not on the analysis of a document. Also, statistical algorithms were presented to analyze documents consisting of characters and graphic regions [9,10], in which recognition of printed characters and some constrained handwritten characters was investigated. The algorithms were based on statistical matching between the reference feature values and the input feature values, thus they are sometimes sensitive to distortion and writing style.…”

Skew detection of track images based on wavelet transform and linear least square fitting

“…Skew detection of track images is necessary for developing the automatic data acquiring system to the broken magnetic tapes. The system is very significant for aviation accident investigations Image skew detection is mainly applied to optical character recognition (OCR) systems and document analysis systems (DAS) [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. For the document image skew detection, many methods have been developed.…”