To make it possible to extract character information with a high accuracy even from a document image obtained by reading a document in which a logo mark or the like overlaps a character portion. By performing binarization processing for a document image obtained by reading a document, a binary image including first pixels representing a color darker than a reference and second pixels representing a color paler than the reference is generated. Then, by changing the pixel among the first pixels included in the generated binary image, whose corresponding pixel's color in the document image is different from a color of a character object within the document, to the second pixel, a binary image in which a background object that overlaps the character object in the document image is removed is generated.

To make it possible to extract character information with a high accuracy even from a document image obtained by reading a document in which a logo mark or the like overlaps a character portion. By performing binarization processing for a document image obtained by reading a document, a binary image including first pixels representing a color darker than a reference and second pixels representing a color paler than the reference is generated. Then, by changing the pixel among the first pixels included in the generated binary image, whose corresponding pixel's color in the document image is different from a color of a character object within the document, to the second pixel, a binary image in which a background object that overlaps the character object in the document image is removed is generated.

A processor may receive an image and determine a number of foreground pixels in the image. The processor may obtain a result of optical character recognition (OCR) processing performed on the image. The processor may identify at least one bounding box surrounding at least one portion of text in the result and overlay the at least one bounding box on the image to form a masked image. The processor may determine a number of foreground pixels in the masked image and a decrease in the number of foreground pixels in the masked image relative to the number of foreground pixels in the image. Based on the decrease, the processor may modify an aspect of the OCR processing for subsequent image processing.

A method includes receiving from a first detection system, an image of a license plate, applying an optical recognition function to characters of the license plate, and assigning a confidence value to each of the characters. The method further includes, in response to determining that the confidence value for a particular character is below a threshold, sending an image of the character to a gamified human reviewer application, receiving from the gamified human reviewer application, a human response indicative of a characteristic of the character, and updating the optical recognition function based on the characteristic of the character.

A processor may receive an image and determine a number of foreground pixels in the image. The processor may obtain a result of optical character recognition (OCR) processing performed on the image. The processor may identify at least one bounding box surrounding at least one portion of text in the result and overlay the at least one bounding box on the image to form a masked image. The processor may determine a number of foreground pixels in the masked image and a decrease in the number of foreground pixels in the masked image relative to the number of foreground pixels in the image. Based on the decrease, the processor may modify an aspect of the OCR processing for subsequent image processing.

A processor may receive an image and determine a number of foreground pixels in the image. The processor may obtain a result of optical character recognition (OCR) processing performed on the image. The processor may identify at least one bounding box surrounding at least one portion of text in the result and overlay the at least one bounding box on the image to form a masked image. The processor may determine a number of foreground pixels in the masked image and a decrease in the number of foreground pixels in the masked image relative to the number of foreground pixels in the image. Based on the decrease, the processor may modify an aspect of the OCR processing for subsequent image processing.

An artificial neuron unit and neural network for processing of input light are described. The artificial neuron unit comprises a modal mixing unit, such as multimode optical fiber, configured for receiving input light and applying selected mixing to light components of two or more modes within the input light and for providing exit light, and a filtering unit configured for applying preselected filter onto said exit light for selecting one or more modes of the exit light thereby providing output light of the artificial neuron unit.

A processor may receive an image and determine a number of foreground pixels in the image. The processor may obtain a result of optical character recognition (OCR) processing performed on the image. The processor may identify at least one bounding box surrounding at least one portion of text in the result and overlay the at least one bounding box on the image to form a masked image. The processor may determine a number of foreground pixels in the masked image and a decrease in the number of foreground pixels in the masked image relative to the number of foreground pixels in the image. Based on the decrease, the processor may modify an aspect of the OCR processing for subsequent image processing.

A processor may receive an image and determine a number of foreground pixels in the image. The processor may obtain a result of optical character recognition (OCR) processing performed on the image. The processor may identify at least one bounding box surrounding at least one portion of text in the result and overlay the at least one bounding box on the image to form a masked image. The processor may determine a number of foreground pixels in the masked image and a decrease in the number of foreground pixels in the masked image relative to the number of foreground pixels in the image. Based on the decrease, the processor may modify an aspect of the OCR processing for subsequent image processing.

A method for detecting an object in a search image, includes: a) providing a pattern vector which describes the object by means of coordinates of characteristic pixels; b) shifting the pattern vector over different positions of the search image; c) determining a success value at each of the different positions; and d) detecting the object at the position on the basis of the success value, wherein each characteristic pixel is assigned a first direction and a second direction that differs from the first direction. The first and second overall intensity values of one-dimensionally arranged pixels are determined in the first and second directions, respectively. A difference value between the first and second overall intensity values is determined in each case, and the success value is determined on the basis of the respective difference values.