An image processing apparatus includes a character determining unit configured to divide the read image into multiple blocks, each of the multiple blocks including multiple characters, and determine an inclination of each of the multiple characters included in each of the multiple blocks, a block processing unit configured to detect a change point block, the change point block being a block including characters having an inclination included in a first inclination interval, a number of the characters being equal to or larger than a first threshold, and including characters having an inclination included in a second inclination interval, a number of the characters being equal to or larger than the first threshold, the second inclination interval being different from the first inclination interval, and a fold determining unit configured to determine that the document is folded if the change point block is detected.

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 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.

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.

A license plate recognition system and a license plate recognition method are provided. The license plate recognition system includes an image capturing module, a determination module and an output module. The image capturing module is utilized for capturing an image of a target object. The determination module is utilized for dividing the image of the target object into a plurality of image blocks. The determination module utilizes the plurality of image blocks to generate feature data and perform a data sorting process on the feature data to generate a first sorting result. The output module outputs the sorting result.

Methods of processing video are presented to generate signatures for motion segmented regions over two or more frames. Two frames are differenced using an adaptive threshold to generate a two-frame difference image. The adaptive threshold is based on a motion histogram analysis which may vary according to motion history data. Also, a count of pixels is determined in image regions of the motion adapted two-frame difference image which identifies when the count is not within a threshold range to modify the motion adaptive threshold. A motion history image is created from the two-frame difference image. The motion history image is segmented to generate one or more motion segmented regions and a descriptor and a signature are generated for a selected motion segmented region.

An electronic device and method are disclosed herein. The electronic device includes: a processor, and a memory operatively connected to the processor. The processor implements the method, including: receive an input of handwritten text including characters, set corpus line key points indicating a substantial-top positions of the characters included in the handwritten text and base line key points indicating a substantial bottom positions of the characters, calculate at least a first feature value based on at least one corpus line key point from among the corpus line key points, and a second feature value based on at least one extracted base line key point from among the base line key points, respectively, input the calculated first feature value into a first neural network to cause the first neural network to generate a first result value, input the calculated second feature value into a second neural network to cause the second neural network to generate a second result value, and input the first result value and the second result value into a fully connected neural network to generate a third result value.

A method and a system of capturing an image of a card having a magnetic stripe is provided. The method includes obtaining a first image by an imaging device of the card, obtaining a plurality of images of the card via color delta analysis, and obtaining a third image of the card by comparing the first and the plurality of images.

A character recognition device includes an acquisition means configured to acquire an image containing a character region, a first recognition means configured to recognize a character from the character region by a first recognition method, a setting means configured to set reference lines along an alignment direction of the characters and passing through a specified position in each character, a second recognition means configured to recognize a character by a second recognition method, the second recognition method being a method that recognizes a character from an image within a recognition window by scanning in a recognition target region in an image while changing a size of the recognition window, and configured to set a position or a height in a vertical direction of the recognition window based on the reference lines, and an output means configured to output a word composed of characters recognized by the second recognition means.

An image processing apparatus includes a reading unit configured to read an image of a document and generate image data, a first binarization unit configured to generate binary image data by performing halftone processing on the image data generated by the reading unit, a smoothing unit configured to perform smoothing processing on a character region of the binary image data, a second binarization unit configured to perform binarization processing on the character region having been subjected to the smoothing processing by the smoothing unit, and a character recognition unit configured to perform character recognition processing on the character region having been subjected to the binarization processing by the second binarization unit.