According to one embodiment, a reading system includes a processing device. The processing device includes an extractor, a line thinner, a setter, and an identifier. The extractor extracts a partial image from an input image. A character of a segment display is imaged in the partial image. The segment display includes a plurality of segments. The line thinner thins a cluster of pixels representing a character in the partial image. The setter sets, in the partial image, a plurality of determination regions corresponding respectively to the plurality of segments. The identifier detects a number of pixels included in the thinned cluster for each of the plurality of determination regions, and identifies the character based on a detection result.

A content manipulation application provides a graphical interface for editing graphical content. The graphical interface includes first and second control elements for performing first and second manipulations of the graphical content. If the first control element is selected, the content management application switches the graphical interface to a trackpad mode. The trackpad mode disables the second control element and thereby prevents the second control element from performing the second manipulation. While the graphical interface is in the trackpad mode, the content management application receives an input in an input area that lacks the first control element and performs the first manipulation of the graphical content responsive to receiving the input. Subsequent to the first manipulation being performed, the graphical interface is switched out of the trackpad mode, thereby enabling the second control element to perform the second manipulation.

The invention provides an offline handwriting individual recognition system and method based on two-dimensional dynamic features. The method comprises: obtaining a first handwriting image corresponding to suspicious handwriting and second handwriting images corresponding to a plurality of sample handwriting; pre-processing the first handwriting image and the second handwriting images to obtain a first pre-processed image and second pre-processed images; extracting a first skeleton image and second skeleton images from the first pre-processed image and the second pre-processed images; obtaining a first writing trajectory and second writing trajectories according to the first skeleton image and the second skeleton images; extracting first dynamic features and second dynamic features according to the first writing trajectory and the second writing trajectories; performing processing to obtain a correlation coefficient between the suspicious handwriting and the sample handwriting, and performing processing to obtain an individual recognition result according to the correlation coefficient.

The invention provides an offline handwriting individual recognition system and method based on two-dimensional dynamic features. The method comprises: obtaining a first handwriting image corresponding to suspicious handwriting and second handwriting images corresponding to a plurality of sample handwriting; pre-processing the first handwriting image and the second handwriting images to obtain a first pre-processed image and second pre-processed images; extracting a first skeleton image and second skeleton images from the first pre-processed image and the second pre-processed images; obtaining a first writing trajectory and second writing trajectories according to the first skeleton image and the second skeleton images; extracting first dynamic features and second dynamic features according to the first writing trajectory and the second writing trajectories; performing processing to obtain a correlation coefficient between the suspicious handwriting and the sample handwriting, and performing processing to obtain an individual recognition result according to the correlation coefficient.

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 text recognition method and apparatus disclosed. 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 vertex pairs, m being a positive integer greater than 2; correcting the polygonal area to m1 rectangular areas to obtain a corrected target text detection area; and performing text recognition on the corrected target text detection area to determine the target text, and outputting the target text.

A text recognition method and apparatus disclosed. 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 vertex pairs, m being a positive integer greater than 2; correcting the polygonal area to m1 rectangular areas to obtain a corrected target text detection area; and performing text recognition on the corrected target text detection area to determine the target text, and outputting the target text.

A binary image of an input image is generated, and a character region within the binary image and a region surrounding each character are acquired as character segmentation rectangle information. A thinning process is executed on a region within the binary image which is identified based on the character segmentation rectangle information to acquire a thinned image. An edge detected image of the region identified based on the character segmentation rectangle information is acquired. Whether each character identified based on the character segmentation rectangle information is a character to be separated from a background by the binarization process or not is determined based on a result of a logical AND of the thinned image and the edge detected image.

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.