Abstract: An electronic device and method receive (for example, from a memory), a grayscale image of a scene of real world captured by a camera of a mobile device. The electronic device and method also receive a color image from which the grayscale image is generated, wherein each color pixel is stored as a tuple of multiple components. The electronic device and method determine a new intensity for at least one grayscale pixel in the grayscale image, based on at least one component of a tuple of a color pixel located in correspondence to the at least one grayscale pixel. The determination may be done conditionally, by checking whether a local variance of intensities is below a predetermined threshold in a subset of grayscale pixels located adjacent to the at least one grayscale pixel, and selecting the component to provide most local variance of intensities.

Abstract: Embodiments disclosed pertain to Optical Character Recognition using Multiple Hypothesis Testing based techniques on images occurring in a variety of settings, including images captured by mobile stations. In some embodiments, a set of bifurcation points for a character cluster in an image may be determined. The character cluster may comprise non-uniformly spaced text or closely spaced text. A plurality of hypotheses may be determined for the character cluster, where each hypothesis is based on a subset of the bifurcation points and comprises a set of words generated from the character cluster. A plurality of scores corresponding to the plurality of hypotheses may be determined, where each score corresponds to a hypothesis, and a hypothesis may be selected from among the plurality of hypotheses based on a score associated with the selected hypothesis.

Abstract: An image of real world is processed to identify blocks as candidates to be recognized. Each block is subdivided into sub-blocks, and each sub-block is traversed to obtain counts, in a group for each sub-block. Each count in the group is either of presence of transitions between intensity values of pixels or of absence of transition between intensity values of pixels. Hence, each pixel in a sub-block contributes to at least one of the counts in each group. The counts in a group for a sub-block are normalized, based at least on a total number of pixels in the sub-block. Vector(s) for each sub-block including such normalized counts may be compared with multiple predetermined vectors of corresponding symbols in a set, using any metric of divergence between probability density functions (e.g. Jensen-Shannon divergence metric). Whichever symbol has a predetermined vector that most closely matches the vector(s) is identified and stored.

Abstract: Certain aspects of the present disclosure relate to techniques for low-complexity encoding (compression) of broad class of signals, which are typically not well modeled as sparse signals in either time-domain or frequency-domain. First, the signal can be split in time-segments that may be either sparse in time domain or sparse in frequency domain, for example by using absolute second order differential operator on the input signal. Next, different encoding strategies can be applied for each of these time-segments depending in which domain the sparsity is present.

Abstract: Systems, apparatuses, and methods to relate images of words to a list of words are provided. A trellis based word decoder analyses a set of OCR characters and probabilities using a forward pass across a forward trellis and a reverse pass across a reverse trellis. Multiple paths may result, however, the most likely path from the trellises has the highest probability with valid links. A valid link is determined from the trellis by some dictionary word traversing the link. The most likely path is compared with a list of words to find the word closest to the most.

Abstract: Embodiments disclosed facilitate robust, accurate, and reliable recovery of words and/or characters in the presence of non-uniform lighting and/or shadows. In some embodiments, a method to recover text from image may comprise: expanding a Maximally Stable Extremal Region (MSER) in an image, the neighborhood comprising a plurality of sub-blocks; thresholding a subset of the plurality of sub-blocks in the neighborhood, the subset comprising sub-blocks with text, wherein each sub-block in the subset is thresholded using a corresponding threshold associated with the sub-block; and obtaining a thresholded neighborhood.

Abstract: An electronic device and method use a camera to capture an image of an environment outside followed by identification of regions therein. A subset of the regions is selected, based on attributes of the regions, such as aspect ratio, height, and variance in stroke width. Next, a number of angles that are candidates for use as skew of the image are determined (e.g. one angle is selected for each region. based on peakiness of a histogram of the region, evaluated at different angles). Then, an angle that is most common among these candidates is identified as the angle of skew of the image. The just-described identification of skew angle is performed prior to classification of any region as text or non-text. After skew identification, at least all regions in the subset are rotated by negative of the skew angle, to obtain skew-corrected regions for use in optical character recognition.

Abstract: Systems, apparatus and methods for extracting lower modifiers from a word image, before performing optical character recognition (OCR), based on a plurality of tests comprising a first test, a second test and a third test are presented. The method obtains the word image and performing a plurality of tests (e.g., a first test, a second test and a third test). The first test determines whether a vertical line spanning the height of the word image exists. The second test determines whether a jump of a number of components in the lower portion of the word image exists. The third test determines sparseness in a lower portion of the word image. The plurality of tests may run sequentially and/or in parallel. Results from the plurality of tests are used to decide whether a lower modifier exists by comparing and accumulating test results from the plurality of tests.

Abstract: Certain aspects of the present disclosure relate to techniques for low-complexity encoding (compression) of broad class of signals, which are typically not well modeled as sparse signals in either time-domain or frequency-domain. First, the signal can be split in time-segments that may be either sparse in time domain or sparse in frequency domain, for example by using absolute second order differential operator on the input signal. Next, different encoding strategies can be applied for each of these time-segments depending in which domain the sparsity is present.

Abstract: In several aspects of described embodiments, an electronic device and method use a camera to capture an image or a frame of video of an environment outside the electronic device followed by identification of blocks of regions in the image. Each block that contains a region is checked, as to whether a test for presence of a line of pixels is met. When the test is met for a block, that block is identified as pixel-line-present. Pixel-line-present blocks are used to identify blocks that are adjacent. One or more adjacent block(s) may be merged with a pixel-line-present block when one or more rules are found to be satisfied, resulting in a merged block. The merged block is then subject to the above-described test, to verify presence of a line of pixels therein, and when the test is satisfied the merged block is processed normally, e.g. classified as text or non-text.

Abstract: Systems, apparatuses, and methods to relate images of words to a list of words are provided. A trellis based word decoder analyses a set of OCR characters and probabilities using a forward pass across a forward trellis and a reverse pass across a reverse trellis. Multiple paths may result, however, the most likely path from the trellises has the highest probability with valid links. A valid link is determined from the trellis by some dictionary word traversing the link. The most likely path is compared with a list of words to find the word closest to the most.

Abstract: Disclosed embodiments pertain to a method on a Mobile Station (MS) for input of text for abugida writing systems. In some embodiments, the method may comprise obtaining a base character by performing Optical Character Recognition (OCR) on written user-input on the MS. A conjunct character may also be obtained by applying one or more functional or diacritical operators to the base character. The conjunct character may then be displayed.

Abstract: A difference in intensities of a pair of pixels in an image is repeatedly compared to a threshold, with the pair of pixels being separated by at least one pixel (“skipped pixel”). When the threshold is found to be exceeded, a selected position of a selected pixel in the pair, and at least one additional position adjacent to the selected position are added to a set of positions. The comparing and adding are performed multiple times to generate multiple such sets, each set identifying a region in the image, e.g. an MSER. Sets of positions, identifying regions whose attributes satisfy a test, are merged to obtain a merged set. Intensities of pixels identified in the merged set are used to generate binary values for the region, followed by classification of the region as text/non-text. Regions classified as text are supplied to an optical character recognition (OCR) system.

Abstract: Embodiments disclosed pertain to Optical Character Recognition using Multiple Hypothesis Testing based techniques on images occurring in a variety of settings, including images captured by mobile stations. In some embodiments, a set of bifurcation points for a character cluster in an image may be determined. The character cluster may comprise non-uniformly spaced text or closely spaced text. A plurality of hypotheses may be determined for the character cluster, where each hypothesis is based on a subset of the bifurcation points and comprises a set of words generated from the character cluster. A plurality of scores corresponding to the plurality of hypotheses may be determined, where each score corresponds to a hypothesis, and a hypothesis may be selected from among the plurality of hypotheses based on a score associated with the selected hypothesis.

Abstract: An electronic device and method receive (for example, from a memory), a grayscale image of a scene of real world captured by a camera of a mobile device. The electronic device and method also receive a color image from which the grayscale image is generated, wherein each color pixel is stored as a tuple of multiple components. The electronic device and method determine a new intensity for at least one grayscale pixel in the grayscale image, based on at least one component of a tuple of a color pixel located in correspondence to the at least one grayscale pixel. The determination may be done conditionally, by checking whether a local variance of intensities is below a predetermined threshold in a subset of grayscale pixels located adjacent to the at least one grayscale pixel, and selecting the component to provide most local variance of intensities.

Abstract: Certain aspects of the present disclosure relate to a method for quantizing signals and reconstructing signals, and/or encoding or decoding data for storage or transmission. Points of a signal may be determined as local extrema or points where an absolute rise of the signal is greater than a threshold. The tread and value of the points may be quantized, and certain of the quantizations may be discarded before the quantizations are transmitted. After being received, the signal may be reconstructed from the quantizations using an iterative process.

Abstract: An electronic device and method use a camera to capture an image of an environment outside the electronic device followed by identification of regions, based on pixel intensities in the image. At least one processor automatically computes multiple values of an indicator of skew in multiple regions in the image respectively. The multiple values are specific to the multiple regions, and thereafter used to determine whether unacceptable skew is present across the regions, e.g. globally in the image as a whole. When skew is determined to be unacceptable, user input is requested to correct the skew, e.g. by displaying on a screen, a symbol and receiving user input (e.g. by rotating an area of touch or rotating the electronic device) to align a direction of the symbol with a direction of the image, and then the process may repeat (e.g. capture image, detect skew, and if necessary request user input).

Abstract: An electronic device and method capture multiple images of a scene of real world at a several zoom levels, the scene of real world containing text of one or more sizes. Then the electronic device and method extract from each of the multiple images, one or more text regions, followed by analyzing an attribute that is relevant to OCR in one or more versions of a first text region as extracted from one or more of the multiple images. When an attribute has a value that meets a limit of optical character recognition (OCR) in a version of the first text region, the version of the first text region is provided as input to OCR.

Abstract: An electronic device and method identify regions that are likely to be text in a natural image or video frame, followed by processing as follows: lines that are nearly vertical are automatically identified in a selected text region, oriented relative to the vertical axis within a predetermined range ?max_theta to +max_theta, followed by determination of an angle ? of the identified lines, followed by use of the angle ? to perform perspective correction by warping the selected text region. After perspective correction in this manner, each text region is processed further, to recognize text therein, by performing OCR on each block among a sequence of blocks obtained by slicing the potential text region. Thereafter, the result of text recognition is used to display to the user, either the recognized text or any other information obtained by use of the recognized text.

Abstract: A difference in intensities of a pair of pixels in an image is repeatedly compared to a threshold, with the pair of pixels being separated by at least one pixel (“skipped pixel”). When the threshold is found to be exceeded, a selected position of a selected pixel in the pair, and at least one additional position adjacent to the selected position are added to a set of positions. The comparing and adding are performed multiple times to generate multiple such sets, each set identifying a region in the image, e.g. an MSER. Sets of positions, identifying regions whose attributes satisfy a test, are merged to obtain a merged set. Intensities of pixels identified in the merged set are used to generate binary values for the region, followed by classification of the region as text/non-text. Regions classified as text are supplied to an optical character recognition (OCR) system.