Abstract: A pressure-sensitive adhesive fastener (100) is overlapped with a target object (150) on one surface (110a) side provided with a pressure-sensitive adhesive layer (121) and protruding members (130) to be coupled with the target object (150). With the pressure-sensitive adhesive fastener (100) coupled with the target object (150), the pressure-sensitive adhesive layer (121) is bonded to the target object (150), and part of column sections (131) and insertion sections (132) of the protruding members (130) enter gaps in the target object (150). This entrance creates such a mode that the protruding members (130) are stuck into the target object (150), resulting in a prevention of a shift in position between the pressure-sensitive adhesive fastener (100) and the target object (150).

Abstract: A mechanical fastener is disclosed that includes a thermoplastic backing and multiple, upstanding fastening elements that have a post with a proximal end attached to the thermoplastic backing and a distal end comprising a cap larger in area than a cross-sectional area of the post. The basis weight of the mechanical fastener is in a range from 25 grams per square meter to 75 grams per square meter, and the height of the multiple, upstanding fastening elements is up to 300 micrometers. Fastening systems and disposable absorbent articles including the mechanical fastener are also disclosed.

Abstract: A mechanical fastener is disclosed that includes a thermoplastic backing and multiple, upstanding fastening elements that have a post with a proximal end attached to the thermoplastic backing and a distal end comprising a cap larger in area than a cross-sectional area of the post. The basis weight of the mechanical fastener is in a range from 25 grams per square meter to 75 grams per square meter, and the height of the multiple, upstanding fastening elements is up to 300 micrometers. Fastening systems and disposable absorbent articles including the mechanical fastener are also disclosed.

Abstract: An output mechanism of a character recognition device includes a category classifier for classifying image data of characters to be recognized for each category recognized in character recognition processing, a clustering processor for determining feature values related to shapes of characters included in the image data in each category classified by the category classifier, and for classifying the image data into one or more clusters based on the feature values, and a screen creator for creating a confirmation screen for displaying the image data for each cluster classified by the clustering processor.

Abstract: An output mechanism of a character recognition device includes a category classifier for classifying image data of characters to be recognized for each category recognized in character recognition processing, a clustering processor for determining feature values related to shapes of characters included in the image data in each category classified by the category classifier, and for classifying the image data into one or more clusters based on the feature values, and a screen creator for creating a confirmation screen for displaying the image data for each cluster classified by the clustering processor.

Abstract: An output mechanism of a character recognition device includes a category classifier for classifying image data of characters to be recognized for each category recognized in character recognition processing, a clustering processor for determining feature values related to shapes of characters included in the image data in each category classified by the category classifier, and for classifying the image data into one or more clusters based on the feature values, and a screen creator for creating a confirmation screen for displaying the image data for each cluster classified by the clustering processor.

Abstract: An output mechanism of a character recognition device includes a category classifier for classifying image data of characters to be recognized for each category recognized in character recognition processing, a clustering processor for determining feature values related to shapes of characters included in the image data in each category classified by the category classifier, and for classifying the image data into one or more clusters based on the feature values, and a screen creator for creating a confirmation screen for displaying the image data for each cluster classified by the clustering processor.

Abstract: Methods and apparatus are provided for removing ruled lines from a binary image containing character portions and ruled line portions, comprising the following steps. First, horizontal black runs in the bit-map image are detected and the detected black runs portions are stored as a run-length table comprising values of the horizontal start point and the length from the starting point of each black run for each vertical position. Then, based on the run-length table, black runs exceeding a predetermined threshold value are removed from the image to remove the ruled line comprising those black runs. Then, residual noise is eliminated from the image after the removal of the ruled lines. Then, in the image after the elimination of the residual noise, vertically disconnected components of a character are connected. Then, a portion of the character which is deleted by ruled line removal process is extracted from the image after the connection of vertically disconnected components of the character.

Abstract: An interval ?0, 1! of the output of neural network is equally divided into M (M being an integer of two or more), and the numbers or frequencies of data for the correct/incorrect patterns contained in the i-th interval ?(i-1)/M, i/M! are .mu.1i and .mu.0i, respectively (where, i=1 . . . M). In this case, if this network provides an output contained in the i-th interval to unknown pattern data, this pattern is stored as a likelihood conversion table so that the pattern outputs likelihood P1i, which is a category, in an equation P1i=(.mu.1i+1)/(.mu.1i+.mu.0i+2). Then, when a value contained in the i-th interval ?(i-1)/M, i/M! is output from a neural network, the likelihood convertor receives it as an input and outputs P1i which is so to speak normalized likelihood.

Abstract: A system and method for electronically segmenting character components on character-containing documents involving first scanning a document and quantizing the image information obtained by the scanning into two levels, e.g., black and white, by 1 and 0 bits, and from the quantized information, m-bit OR groups are generated by sequentially ORing every m-th bit in a first direction of the quantized image, where m is an integer equal to or greater than two. The black (character) bits in each of the m-bit OR groups are counted and processed using the steps of: sequentially calculating sums of n consecutive count values by shifting one by one the count values obtained by the counting along a second direction perpendicular to the first direction; and then, segmenting character components by comparing the sums with a predetermined threshold value.