Audio distortion compensation methods to improve accuracy and efficiency of audio content identification are described. The method is also applicable to speech recognition. Methods to detect the interference from speakers and sources, and distortion to audio from environment and devices, are discussed. Additional methods to detect distortion to the content after performing search and correlation are illustrated. The causes of actual distortion at each client are measured and registered and learnt to generate rules for determining likely distortion and interference sources. The learnt rules are applied at the client, and likely distortions that are detected are compensated or heavily distorted sections are ignored at audio level or signature and feature level based on compute resources available. Further methods to subtract the likely distortions in the query at both audio level and after processing at signature and feature level are described.

A method for generating a plurality of sets of training image data for training a machine learning model includes: (a) acquiring object image data representing an object image; (b) dividing the object image into T number of partial object images by dividing a region of the object image into T number of partial regions corresponding to respective ones of T number of partial color ranges; (c) generating a plurality of sets of color-modified object image data representing respective ones of a plurality of color-modified object images by performing an adjustment process on the object image data, the adjustment process including a color modification process to modify colors of at least one of the T number of partial object images; and (d) generating the plurality of sets of training image data using one or more sets of background image data and the plurality of sets of color-modified object image data.

A computer-implemented method for automatically analyzing handwritten text to determine a mismatch between a purported writer and an actual writer is disclosed. The method comprises receiving two samples of digitized handwriting each allegedly created by one individual and received and entered into a digital system by another. The method further comprises performing a series of feature extractions to convert the samples into two vectors of extracted features; automatically clustering a set of vectors such that the first vector and the second vector are assigned to the same cluster among multiple clusters, based on vector similarity; and automatically determining that a same individual being associated with both the first and second samples indicates a heightened probability that the individual fraudulently created both samples. Finally, the method comprises automatically transmitting a message to flag additional samples of digitized handwriting entered into a digital system as possibly fraudulent.

A decision system includes an identifier and a decider. The identifier identifies, based on a captured image generated by an image capturing unit attached to a tool, a work target shot as a subject of the captured image as one of a plurality of work targets. The decider decides whether a first work target included in the plurality of work targets is a mistakable work target by comparing, by reference to at least one reference image, the first work target with a second work target also included in the work targets. If the second work target is similar to the first work target, the second work target makes the first work target the mistakable work target difficult to identify based on the captured image. The at least one reference image belongs to a plurality of reference images corresponding one to one to the plurality of work targets.

An image processing apparatus capable of removing an unnecessary area from a scanned image and thereby making it easy to recognize a necessary area of the scanned image. The image processing apparatus includes a calculation unit that calculates a density value histogram based on an acquired scanned image, a setting unit that sets a necessary area density that has a predetermined value range around the most frequently appearing density value having the highest appearance frequency in the density value histogram, and sets a binarization threshold value based on the necessary area density, and a control unit that controls execution of binarization processing for correcting an area of the scanned image, in which density values are equal to or higher than the threshold value, to black, and correcting an area of the scanned image, in which density values are lower than the threshold value, to white.

Systems can be configured for detecting license plates and recognizing characters in license plates. In an example, a system can receive an image and identify one or more regions in the image that include a license plate. Character recognition can be performed in the one or more regions to determine contents of a candidate license plate. Location-specific information about a license plate format can be used together with the determined contents of the candidate license plate to determine if the recognized characters are valid.

This disclosure relates generally to method and system for detecting and extracting price region from digital flyers and promotions. In retail business, extracting price information from digital flyers is crucial for complex nature of flyers having large variety of formats, color scheme, font styles, variable text information and thereof. The method of the present disclosure detects a text region comprising a price information from a set of digital flyers and promotions received as input images. Further, each text region is converted into a two-color text comprising of a set of white pixels and a set of black pixels. Further, underlying price from the price region of the two-color text is detected and price is extracted from the price region of each input image. Additionally, the price region detection function detects price region accurately and extracts price values having an irregular font size.

In one aspect, a computerized method for anti-counterfeiting solution using a machine learning (ML) model includes the step of providing a pre-defined set of feature detection rules, a pre-defined set of edge detection rules, a pre-defined threshold percentage, an original seal, an original fingerprint of the original seal, and a pre-trained fingerprint identification model. The pre-trained fingerprint identification model is trained by a specified ML algorithm using one or more digital images of the original seal. With a digital camera of a scanning device, the method scans a seal whose authenticity is to be determined. The seal is used to secure a transportation container. The method uses the pre-defined set of feature detection rules to detect and extract an extracted feature image at a specified position on the seal. The method breaks down the extracted feature image of the seal into a ‘kn’ number of sub-images by forming a ‘k’ rows x ‘n’ columns of a grid of the extracted feature image. The method implements the pre-defined set of edge detection rules to extract an edge structure of at least one object in each of the ‘kn’ number of sub-images. The method generates a set of unique fingerprints by specified steps. The method includes generating a unique fingerprint corresponding to a unique number or a feature based on each extracted edge structure. For the set of unique fingerprints, the method generates a match percentage for the set of unique fingerprints using the pre-trained fingerprint identification model. The match percentage corresponds to a matching proportion between each unique fingerprint generated for the seal being verified and the original fingerprint of the original seal on which the pre-trained fingerprint identification model is trained.

A captured image contains a region of interest (ROI) including a plurality of characters to be recognized as text, and non-ROI content to be excluded from the OCR. The captured image is preprocessed to detect and locate the ROI in the captured image, and to determine a boundary of the ROI, including transforming the captured image to a first feature descriptor representation (FDR), and performing a comparison between the first FDR and at least one ROI template that includes at least a second FDR of a representative ROI image. The preprocessing produces an output to be provided to an OCR engine to perform autonomous OCR processing of the ROI while ignoring the non-ROI content based on the determined boundary of the ROI.

The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes determining valid pixel-based pattern(s) that are included in valid reference images. Fraudulent pixel-based pattern(s) that are included in fraudulent reference images are determined. A request to classify an image is received. A determination is made as to whether pixel values in the image match a valid pixel-based pattern or a fraudulent pixel-based pattern. In response to determining that the pixel values match a valid pixel-based pattern, a likelihood of classifying the first image as a valid image is increased. In response to determining that the pixel values match a fraudulent pixel-based pattern, a likelihood that the image as a fraudulent image is increased. The image is classified in response to the request as either a valid image or a fraudulent image based on the likelihoods.