An image processing apparatus according to this embodiment performs, based on a job setting, conversion of a pixel value of a partial region in an input original image into a predetermined value on image data of the original image, as needed. Subsequently, this image processing apparatus generates a histogram representing the density signal distribution of the image data of the original image or the converted image data. Note that if the above-described conversion is performed, this image processing apparatus corrects the generated histogram by subtracting, from a count of the predetermined number of the generated histogram, the number of sampling points counted in the above-described partial region when the histogram is generated.

A binary image of an input image is generated, and a character region within the binary image and a region surrounding each character are acquired as character segmentation rectangle information. A thinning process is executed on a region within the binary image which is identified based on the character segmentation rectangle information to acquire a thinned image. An edge detected image of the region identified based on the character segmentation rectangle information is acquired. Whether each character identified based on the character segmentation rectangle information is a character to be separated from a background by the binarization process or not is determined based on a result of a logical AND of the thinned image and the edge detected image.

A binary image of an input image is generated, and a character region within the binary image and a region surrounding each character are acquired as character segmentation rectangle information. A thinning process is executed on a region within the binary image which is identified based on the character segmentation rectangle information to acquire a thinned image. An edge detected image of the region identified based on the character segmentation rectangle information is acquired. Whether each character identified based on the character segmentation rectangle information is a character to be separated from a background by the binarization process or not is determined based on a result of a logical AND of the thinned image and the edge detected image.

An image reading apparatus includes a gray reference member, a reading unit, an adjusting unit, a calibration unit, a storage unit, and a control unit. The control unit is configured to execute a first preprocessing and a second preprocessing. The first preprocessing includes: acquiring first black data from one line worth of image data; acquiring white data; calculating white-black difference data; acquiring first light gray data; and calculating first light gray-black difference data. The second processing includes: acquiring second black data from the one line worth of the image data; acquiring second light gray data; calculating second light gray-black difference data; calculating a change ratio by dividing the second light gray-black difference data by the first light gray-black difference data; and calculating calibration data by multiplying the white-black difference data by the change ratio. Shading of the image data is calibrated in response to the calibration data.

An image reading apparatus includes a gray reference member, a reading unit, an adjusting unit, a calibration unit, a storage unit, and a control unit. The control unit is configured to execute a first preprocessing and a second preprocessing. The first preprocessing includes: acquiring first black data from one line worth of image data; acquiring white data; calculating white-black difference data; acquiring first light gray data; and calculating first light gray-black difference data. The second processing includes: acquiring second black data from the one line worth of the image data; acquiring second light gray data; calculating second light gray-black difference data; calculating a change ratio by dividing the second light gray-black difference data by the first light gray-black difference data; and calculating calibration data by multiplying the white-black difference data by the change ratio. Shading of the image data is calibrated in response to the calibration data.

The present invention provides an image contrast enhancement method, which respectively calculates the absolute values of the differences of the gray scales between the pixels of two adjacent row of the same column and between the pixels of two adjacent column of the same row, and respectively calculates the first gray scale value weight and the second gray scale value weight according to the absolute values. Then, the cumulative calculation and the normalization process are performed according to the first, the second gray scale value weights. Ultimately, the enhancement gray scale table is obtained for redistributing the gray scale values of the respective pixels, which can raise the contrast of the image, reduce the distortion of the image and optimize the display result.

An image reducing unit performs a reduction process for each reduction target area in one of a primary scanning direction and a secondary scanning direction, and subsequently performs a reduction process for each reduction target area in the other of the primary scanning direction and the secondary scanning direction. A binarizing unit binarizes pixel values of pixels in the reduction target area. A characteristic value identifying unit identifies a characteristic value on the basis of a pattern of the binarized pixel values and integrates the characteristic value. When the integrated value of the characteristic value exceeds a threshold value before the image reduction unit finishes the reduction processes, a control unit terminates the reduction process, changes the reduction ratio so as to increase a size of a reduced image, and causes the image reducing unit to perform the reduction process with the changed reduction ratio again.

An image reducing unit performs a reduction process for each reduction target area in one of a primary scanning direction and a secondary scanning direction, and subsequently performs a reduction process for each reduction target area in the other of the primary scanning direction and the secondary scanning direction. A binarizing unit binarizes pixel values of pixels in the reduction target area. A characteristic value identifying unit identifies a characteristic value on the basis of a pattern of the binarized pixel values and integrates the characteristic value. When the integrated value of the characteristic value exceeds a threshold value before the image reduction unit finishes the reduction processes, a control unit terminates the reduction process, changes the reduction ratio so as to increase a size of a reduced image, and causes the image reducing unit to perform the reduction process with the changed reduction ratio again.

An image processing apparatus for processing image data to be used for image formation, comprises: a black background area determination unit configured to determine a black background area formed from a plurality of black lines using the number of consecutive black lines and the number of consecutive lines adjacent to the black lines and other than the black lines out of the image data; and a thinning processing unit configured to perform thinning processing for the black background area by switching a thinning pattern to convert a value of a pixel based on the number of lines included in the black background area determined by the black background area determination unit.

The present invention provides an image contrast enhancement method, which respectively calculates the absolute values of the differences of the gray scales between the pixels of two adjacent row of the same column and between the pixels of two adjacent column of the same row, and respectively calculates the first gray scale value weight and the second gray scale value weight according to the absolute values. Then, the cumulative calculation and the normalization process are performed according to the first, the second gray scale value weights. Ultimately, the enhancement gray scale table is obtained for redistributing the gray scale values of the respective pixels, which can raise the contrast of the image, reduce the distortion of the image and optimize the display result.