An information processing apparatus includes a touchscreen display that detects, as a detection position, contact of an object on a screen of the touchscreen display and a processor coupled to the touchscreen display. The processor is programmed to acquire a plurality of first detection positions on the screen, calculate a movement parameter representing a movement of the object during the detection of the plurality of first detection positions, and select based on the movement parameter a first smoothing algorithm or a second smoothing algorithm. The first smoothing algorithm and the second smoothing algorithm are different from each other in a processing delay between detecting a particular detection position and output a smoothed detection position corresponding to that particular detecting position. The touchscreen display displays a movement locus on the screen based on the selected smoothing algorithm.

According to an embodiment, a reading system includes a reader and a calculator. The reader reads, from a character image, a character that is displayed by a segment display. The calculator performs one of first, second, third, or fourth processing. In the first processing, the calculator calculates a first score based on a state of pixels of the character. In the second processing, the calculator calculates a second score based on a match ratio between the pixels and the extracted pixels. In the third processing, the calculator calculates a third score based on a ratio of a length of the character image in first and second direction. In the fourth processing, the calculator calculates a fourth score based on a comparison result between the detected result and preset patterns. The calculator calculates a certainty of the reading by using one of the first, second, third, or fourth score.

According to an embodiment, a reading system includes a reader and a calculator. The reader reads, from a character image, a character that is displayed by a segment display. The calculator performs one of first, second, third, or fourth processing. In the first processing, the calculator calculates a first score based on a state of pixels of the character. In the second processing, the calculator calculates a second score based on a match ratio between the pixels and the extracted pixels. In the third processing, the calculator calculates a third score based on a ratio of a length of the character image in first and second direction. In the fourth processing, the calculator calculates a fourth score based on a comparison result between the detected result and preset patterns. The calculator calculates a certainty of the reading by using one of the first, second, third, or fourth score.

A method for template matching can include iteratively selecting a template set of points to project over a centerline of a candidate symbol; conducting a template matching analysis; assigning a score to each template set; and selecting a template set with a highest assigned score. For example, the score can depend on proximity of the template points to a center and/or boundaries of a principal tracing path of the symbol. Additionally, one or more template sets having a top rank can be selected for a secondary analysis of proximity of the template points to a boundary of a printing of the symbol. The method can further include using the template with the highest score to interpret the candidate symbol.

A method for template matching can include iteratively selecting a template set of points to project over a centerline of a candidate symbol; conducting a template matching analysis; assigning a score to each template set; and selecting a template set with a highest assigned score. For example, the score can depend on proximity of the template points to a center and/or boundaries of a principal tracing path of the symbol. Additionally, one or more template sets having a top rank can be selected for a secondary analysis of proximity of the template points to a boundary of a printing of the symbol. The method can further include using the template with the highest score to interpret the candidate symbol.

A typeface design system displays, using a typeface design interface, one or more control points for modifying one or more curves of a design that define an outline of an input character. The typeface design system accesses a machine-learning model trained to recognize the input character as a reference character. The typeface design system receives an input modifying the design including a change in position of an input control point of the one or more control points from a first position to a second position. The typeface design system determines, by the trained machine-learning model, that the reference character does not match the input character having the modified design based on determining that the second position is outside of a particular region defined by the machine-learning model. The typeface design system rejects the input and maintains the design of the input character as displayed prior to receiving the input.

A method for template matching can include iteratively selecting a template set of points to project over a centerline of a candidate symbol; conducting a template matching analysis; assigning a score to each template set; and selecting a template set with a highest assigned score. For example, the score can depend on proximity of the template points to a center and/or boundaries of a principal tracing path of the symbol. Additionally, one or more template sets having a top rank can be selected for a secondary analysis of proximity of the template points to a boundary of a printing of the symbol. The method can further include using the template with the highest score to interpret the candidate symbol.

A text recognition method and apparatus that relate to the field of information processing technologies are provided. This effectively resolves a low recognition rate of curved text. The text recognition method includes: obtaining a to-be-detected image; determining a target text detection area in the to-be-detected image, where the target text detection area includes target text in the to-be-detected image, and the target text detection area is a polygonal area including m (m is a positive integer greater than 2) vertex pairs; correcting the polygonal area to m−1 rectangular areas to obtain a corrected target text detection area; and performing text recognition on the corrected target text detection area, and outputting the target text.

A method for template matching can include iteratively selecting a template set of points to project over a centerline of a candidate symbol; conducting a template matching analysis; assigning a score to each template set; and selecting a template set with a highest assigned score. For example, the score can depend on proximity of the template points to a center and/or boundaries of a principal tracing path of the symbol. Additionally, one or more template sets having a top rank can be selected for a secondary analysis of proximity of the template points to a boundary of a printing of the symbol. The method can further include using the template with the highest score to interpret the candidate symbol.

The present invention provides a new image processing device that is robust against a change in capturing conditions. An image processing device 100 has an acquisition unit 110 for acquiring an image, a smoothing unit 120 for smoothing the acquired image with a prescribed smoothing level, a binarization unit 130 for binarizing the smoothed image, and a control unit 140 for making the smoothing unit 120 execute multiple sessions of smoothing differing in the smoothing level.