An image processing apparatus has a color image, the image data being constituted by multiple pixels, each of the multiple pixels having a gradation value, and a controller, which is configured to generate a histogram of index values corresponding to brightness values of the multiple pixels constituting the image data, set an original threshold value based on the histogram which is referred to for binarization, detect a mound-shaped part, in the histogram, satisfying a particular condition, set an adjusting direction in which the original threshold value is to be adjusted, set the index value at a base on a particular direction side of a particular mound-shaped part which is one of mound-shaped parts existing on the adjusting direction side with respect to the original threshold value in the histogram as an adjusted threshold value, and apply a binarizing process to the image data using the adjusted threshold value.

A search area is set on a recognition target image, cutout areas are set at a plurality of positions in the search area, images corresponding to the plurality of set cutout areas are extracted, similarities of candidate characters obtained by comparison between the extracted images and dictionary data is weighted in accordance with the positions of the cutout areas. In such a manner, evaluation values of the candidate characters are obtained, and a candidate character with the highest evaluation value among the obtained candidate characters is output as a recognition result. Further, a search area relating to a next character is set based on position information about the cutout area corresponding to the recognition result.

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.

A text image processing method and a text image processing apparatus are provided. In some embodiments, a text image processing method includes: preprocessing a text image to obtain a binary image, where the binary image includes multiple connected regions; acquiring a convex hull corresponding to each of the connected regions with a convex hull algorithm; acquiring a character region circumscribing the convex hull; performing character segmentation on the acquired character region to obtain multiple character blocks; and merging the character blocks based on heights of the character blocks to obtain word blocks of the text image.

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.

An image processing apparatus counts at least one of the number of pixels having an identical color to a target pixel, the number of pixels having a similar color to the target pixel, and the number of pixels having a different color from the target pixel in a target window, and determines an attribute of the target pixel based on a result of the counting.

An electronic computing device, and an image analysis method and a computer program product thereof are disclosed herein. The electronic computing device determines whether an image is a photograph, and when the image is a photograph, identifies at least one character block from the image, and divides the at least one character block into a plurality of sub-blocks. The electronic computing device further compares the similarity of the sub-blocks, and determines that the image data is a processed image if the number of times the similarity exceeds a similarity threshold reaches a count threshold.

A method of generating and optimizing a codebooks for document analysis comprises: receiving a first set of document images; extracting a plurality of keypoint regions from each document image of the first set of document images; calculating local descriptors for each keypoint region of the extracted keypoint regions; clustering the local descriptors such that each center of a cluster of local descriptors corresponds to a respective visual word; generating a codebook containing a set of visual words; and optimizing the codebook by maximizing mutual information (MI) between a target field of a second set of document images and at least one visual word of the set of visual words.

A method for recognizing characters in an image of a document having at least one alphanumeric field. The method has the steps of: segmenting the image to identify objects therein; defining a bounding box around each object; making multiple successive selections based on different descriptors and dividing the bounding boxes into a plurality of cells for each of which a texture descriptor is determined as a gradient oriented histogram; performing character recognition on the finally selected bounding boxes. A device for implementing the method.

An image processing apparatus determines whether each of pixels in a scanned image is a color pixel or a monochrome pixel, determines whether each of blocks including the multiple pixels in the scanned image is a color block or a monochrome block, based on determination results of the respective pixels, and determines that the scanned image is a color image in a case where an arrangement pattern of blocks determined to be color blocks matches a predetermined pattern.