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.

Certain embodiments involve automatically controlling modifications to typeface designs. For example, a typeface design application provides a design interface for modifying a design of an input character from a typeface. The typeface design application accesses a machine-learning model that is trained, using multiple training typefaces, to recognize the input character as a reference character. The typeface design application receives, via the design interface, an input modifying the design of the input character. The typeface design application determines that the machine-learning model cannot match the reference character to the input character having a modified design. The typeface design application outputs, via the design interface, an indicator that the input character having the modified design is not recognized as the reference character.

The present disclosure provides a method and system for identifying a train number and train type. The method includes: continuously photographing a train under inspection by using a linear-array camera in motion relative to the train under inspection, and generating a plurality of partial images of the train; splicing the plurality of partial images of the train; correcting distortion of the spliced image; identifying a train number from the corrected image; wherein the correcting distortion of the spliced image includes: extracting a contour of a wheel from the spliced image; obtaining a ratio between a horizontal diameter and a vertical diameter of the wheel from the contour; if the ratio is greater than a first preset threshold, horizontally compressing the spliced image according to the ratio; and if the ratio is smaller than a second preset threshold, horizontally stretching the spliced image.

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 document type recognition apparatus includes an image region separation unit, a smoothing unit, an edge enhancement unit, a histogram creation unit, and a document type recognition unit. The image region separation unit outputs a signal indicative of each region obtained by separating an input image into a character region and a pattern region. The smoothing unit performs smoothing processing to remove halftone dots of a particular number of lines or greater in the pattern region of the input image. The edge enhancement unit outputs an image subjected to edge enhancement processing depending on an amount of edge on an edge portion of the character region in the input image subjected to the smoothing processing. The histogram creation unit creates a histogram of the image subjected to the edge enhancement processing. The document type recognition unit recognizes a document type of the input image by utilizing the histogram.

The present disclosure discloses methods and systems for creating a multi-layered Optical Character Recognition (OCR) document, the multi-layered OCR document facilitates selection of the desired text from the multi-layered OCR document. The method includes receiving a scanned image corresponding to a document, the document includes text information. A binary image is generated from the scanned image. Then, a morphological dilation operation is performed to create one or more text groups, using a horizontal structuring element and a vertical structuring element. Thereafter, OCR operation is applied on each text group to generate a corresponding OCR layer. The one or more OCR layers are then combined while creating a multi-layered OCR document. Finally, the combined OCR layers are superimposed as invisible text layers over the scanned image to create the multi-layered OCR document.

The present disclosure discloses methods and systems for creating a multi-layered Optical Character Recognition (OCR) document, the multi-layered OCR document facilitates selection of the desired text from the multi-layered OCR document. The method includes receiving a scanned image corresponding to a document, the document includes text information. A binary image is generated from the scanned image. Then, a morphological dilation operation is performed to create one or more text groups, using a horizontal structuring element and a vertical structuring element. Thereafter, OCR operation is applied on each text group to generate a corresponding OCR layer. The one or more OCR layers are then combined while creating a multi-layered OCR document. Finally, the combined OCR layers are superimposed as invisible text layers over the scanned image to create the multi-layered OCR document.

A method of detecting a dot code includes: irradiating the dot code and a background surface by a source of radiation capturing by a reader an image of the dot code and the background surface; distinguishing in the image an edge surface from the background surface based on the difference in spectral reflectivity of the edge surface and the background surface; processing the image of the dot code by dilating in the image the edge surface of the dot, until the centre surface has been closed by the dilating; and reading the processed image to derive the dot code from the processed image. An item includes the dot code having a plural dots arranged on the background surface, where a spectral reflectivity of the edge surface differs from a spectral reflectivity of the background surface.