Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to encrypt media for identification. An example apparatus includes a mesh generation controller to generate a mesh based on an encryption matrix; an overlap controller to eliminate overlapping ones of edge connections of the mesh; an edge labelling controller to generate random labels for the edge connections based on a number of remaining ones of the edge connections of the mesh; an encrypted value controller to generate encrypted values based on the random labels; and an encryption controller to encrypt an input matrix with the encrypted values to enable identification of the input matrix.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for text extraction from a receipt image. An example non-transitory computer readable medium comprises instructions that, when executed, cause a machine to at least improve region of interest detection efficiency by converting pixels of an input receipt image from a first format to a second format, generate a binary representation of the input receipt image based on the converted pixels, the binary representation of the input receipt image corresponding to saturation values for respective ones of the converted pixels, calculate mirror data from the binary representation of the input receipt image, and cluster the binary representation of the input receipt image to identify a first set of candidate regions of interest, the candidate regions of interest characterized by portions of the binary representation of the input receipt image having saturation values that satisfy a threshold value.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to encrypt media for identification. An example apparatus accesses a mesh points matrix. Additionally, the example apparatus sets an upper limit equal to a larger of two values of a vector, respective ones of the two values equal to elements of the vector at indices equal to respective values of a coordinate pair of the mesh points matrix and set a lower limit equal to a smaller of the two values of the vector. The example apparatus also appends a value to an encryption vector, the value based on whether the lower limit is equal to zero, the value equal to a lowest integer not less than a quotient of a number based on a square of the upper limit and two and encrypt an input matrix with the encryption vector.

Abstract: Methods, systems, articles of manufacture, and apparatus to extract shape features based on a structural angle template are disclosed. An example apparatus includes a template generator to generate a template based on an input image and calculate a template value based on values in the template; a bit slicer to calculate an OR bit slice and an AND bit slice based on the input image, combine the OR bit slice with the AND bit slice to generate a fused image, group a plurality of pixels of the fused image to generate a pixel window, each pixel of the pixel window including a pixel value, and calculate a window value based on the pixel values of the pixel window; and a comparator to compare the template value with the window value and store the pixel window in response to determining the window value satisfies a similarity threshold with the template value.

Abstract: Methods, systems, articles of manufacture, and apparatus to extract shape features based on a structural angle template are disclosed. An example apparatus includes a template generator to generate a template based on an input image and calculate a template value based on values in the template; a bit slicer to calculate an OR bit slice and an AND bit slice based on the input image, combine the OR bit slice with the AND bit slice to generate a fused image, group a plurality of pixels of the fused image to generate a pixel window, each pixel of the pixel window including a pixel value, and calculate a window value based on the pixel values of the pixel window; and a comparator to compare the template value with the window value and store the pixel window in response to determining the window value satisfies a similarity threshold with the template value.

Abstract: Methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to assess image quality using adaptive non-overlapping mean estimation are disclosed. Example apparatus disclosed herein include a machine learning system to be trained to classify image quality. Disclosed example apparatus also include a feature extractor to apply a blur filter to a first image to determine a blurred image, determine a blur feature value for the first image, the blur feature value to represent an amount the first image differs from the blurred image, and apply a vector of feature values associated with the first image to the machine learning system, the vector of feature values including the blur feature value. Disclosed example apparatus further include an image classifier to classify image quality associated with the first image based on an output of the machine learning system responsive to the vector of feature values associated with the first image.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to encrypt media for identification. An example apparatus includes a mesh generation controller to generate a mesh based on an encryption matrix; an overlap controller to eliminate overlapping ones of edge connections of the mesh; an edge labelling controller to generate random labels for the edge connections based on a number of remaining ones of the edge connections of the mesh; an encrypted value controller to generate encrypted values based on the random labels; and an encryption controller to encrypt an input matrix with the encrypted values to enable identification of the input matrix.

Abstract: Methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to assess image quality using adaptive non-overlapping mean estimation are disclosed. Example apparatus disclosed herein include a machine learning system to be trained to classify image quality. Disclosed example apparatus also include a feature extractor to apply a blur filter to a first image to determine a blurred image, determine a blur feature value for the first image, the blur feature value to represent an amount the first image differs from the blurred image, and apply a vector of feature values associated with the first image to the machine learning system, the vector of feature values including the blur feature value. Disclosed example apparatus further include an image classifier to classify image quality associated with the first image based on an output of the machine learning system responsive to the vector of feature values associated with the first image.

Abstract: Methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to assess image quality using adaptive non-overlapping mean estimation are disclosed. Example image quality assessment methods disclosed herein include replacing respective blocks of pixels of a first image with mean values of the respective blocks of pixels to determine a second image having a smaller size than the first image. Disclosed example image quality assessment methods also include determining a vector of features for the second image. Disclosed example image quality assessment methods further include applying the vector of features to a neural network, and classifying a quality of the first image based on an output of the neural network.

Abstract: Methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to assess image quality using adaptive non-overlapping mean estimation are disclosed. Example image quality assessment methods disclosed herein include replacing respective blocks of pixels of a first image with mean values of the respective blocks of pixels to determine a second image having a smaller size than the first image. Disclosed example image quality assessment methods also include determining a vector of features for the second image. Disclosed example image quality assessment methods further include applying the vector of features to a neural network, and classifying a quality of the first image based on an output of the neural network.