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.

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 system and process of nearsighted (myopia) camera object detection involves detecting the objects through edge detection and outlining or thickening them with a heavy border. Thickening may include making the object bold in the case of text characters. The bold characters are then much more apparent and heavier weighted than the background. Thresholding operations are then applied (usually multiple times) to the grayscale image to remove all but the darkest foreground objects in the background resulting in a nearsighted (myopic) image. Additional processes may be applied to the nearsighted image, such as morphological closing, contour tracing and bounding of the objects or characters. The bound objects or characters can then be averaged to provide repositioning feedback for the camera user. Processed images can then be captured and subjected to OCR to extract relevant information from the image.

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 system and process of nearsighted (myopia) camera object detection involves detecting the objects through edge detection and outlining or thickening them with a heavy border. Thickening may include making the object bold in the case of text characters. The bold characters are then much more apparent and heavier weighted than the background. Thresholding operations are then applied (usually multiple times) to the grayscale image to remove all but the darkest foreground objects in the background resulting in a nearsighted (myopic) image. Additional processes may be applied to the nearsighted image, such as morphological closing, contour tracing and bounding of the objects or characters. The bound objects or characters can then be averaged to provide repositioning feedback for the camera user. Processed images can then be captured and subjected to OCR to extract relevant information from the image.

A system and process of nearsighted (myopia) camera object detection involves detecting the objects through edge detection and outlining or thickening them with a heavy border. Thickening may include making the object bold in the case of text characters. The bold characters are then much more apparent and heaver weighted than the background. Thresholding operations are then applied (usually multiple times) to the grayscale image to remove all but the darkest foreground objects in the background resulting in a nearsighted (myopic) image. Additional processes may be applied to the nearsighted image, such as morphological closing, contour tracing and bounding of the objects or characters. The bound objects or characters can then be averaged to provide repositioning feedback for the camera user. Processed images can then be captured and subjected to OCR to extract relevant information from the image.

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.

A system and process of nearsighted (myopia) camera object detection involves detecting the objects through edge detection and outlining or thickening them with a heavy border. Thickening may include making the object bold in the case of text characters. The bold characters are then much more apparent and heavier weighted than the background. Thresholding operations are then applied (usually multiple times) to the grayscale image to remove all but the darkest foreground objects in the background resulting in a nearsighted (myopic) image. Additional processes may be applied to the nearsighted image, such as morphological closing, contour tracing and bounding of the objects or characters. The bound objects or characters can then be averaged to provide repositioning feedback for the camera user. Processed images can then be captured and subjected to OCR to extract relevant information from the image.

A method is provided for extracting information of interest from a measurement signal having a periodic interference pattern, which includes steps (i) of generating a filtering function representing the frequency components of the interference pattern, by implementing an analysis of an amplitude spectrum of the measurement signal based on morphological criteria, (ii) of applying the filtering function to the measurement signal so as to generate an interference signal constituted essentially by the interference pattern, and (iii) of calculating a filtered signal by carrying out a difference between the measurement signal and the interference signal.

The invention also relates to a device implementing the method.

A system and process of nearsighted (myopia) camera object detection involves detecting the objects through edge detection and outlining or thickening them with a heavy border. Thickening may include making the object bold in the case of text characters. The bold characters are then much more apparent and heavier weighted than the background. Thresholding operations are then applied (usually multiple times) to the grayscale image to remove all but the darkest foreground objects in the background resulting in a nearsighted (myopic) image. Additional processes may be applied to the nearsighted image, such as morphological closing, contour tracing and bounding of the objects or characters. The bound objects or characters can then be averaged to provide repositioning feedback for the camera user. Processed images can then be captured and subjected to OCR to extract relevant information from the image.