Abstract: A system and method for a low complexity and robust spatiotemporal combining for video enhancement is disclosed. In one embodiment, the method includes computing a standard deviation estimate between a video frame and a temporally neighboring frame of the video frame in a video sequence, computing an error value, for each sub-block of pixels within a block of pixels in a current video frame, between pixel values within the sub-block in the current video frame and corresponding motion compensated pixel values in a temporally neighboring video frame of the current video frame, computing a temporal weighting factor for each sub-block of pixels as a function of the error value and the standard deviation estimate, and combining the block of pixels in the current video frame and their corresponding motion compensated pixel values in the temporally neighboring video frame using the computed temporal weighting factor.

Abstract: A scanner is disclosed. The scanner detects when the to surface of the ADF scan glass is contaminated.

Abstract: A system and method for script and orientation detection of images are disclosed. In one example, textual content in the image is extracted. Further, a vertical component run (VCR) and horizontal component run (HCR) are obtained by vectorizing each connected component in the extracted textual content. Furthermore, a concatenated vertical document vectors (VDV) and a horizontal document vector (HDV) are computed. In addition, a substantially matching script and orientation is obtained by comparing the computed concatenated VDV and HDV of the image with reference VDV and HDV associated with each script and orientation, respectively. Also, the substantially matching script and orientation are declared as the script and orientation of the image, if the computed concatenated VDV and HDV of the image substantially match with the reference VDV and HDV of the matching script and orientation, respectively.

Abstract: A system and method for script and orientation detection of images using artificial neural networks (ANNs) are disclosed. In one example, textual content in the image is extracted. Further, a vertical component run (VCR) and horizontal component run (HCR) are obtained by vectorizing each connected component in the extracted textual content. Furthermore, a zonal density run (ZDR) is obtained for each connected component in the extracted textual content. In addition, a concatenated vertical document vector (VDV), horizontal document vector (HDV), and zonal density vector (ZDV) is computed by normalizing the obtained VCR, HCR, and ZDR, respectively, for each connected component. Moreover, the script in the image is determined using a script detection ANN module and the concatenated VDV, HDV, and ZDV of the image. Also, the orientation of the image is determined using an orientation detection ANN module and the concatenated VDV, HDV, and ZDV of the image.

Abstract: A system and method for script and orientation detection of images are disclosed. In one example, textual content in the image is extracted. Further, a vertical component run (VCR) and horizontal component run (HCR) are obtained by vectorizing each connected component in the extracted textual content. Furthermore, a concatenated vertical document vectors (VDV) and a horizontal document vector (HDV) are computed. In addition, a substantially matching script and orientation is obtained by comparing the computed concatenated VDV and HDV of the image with reference VDV and HDV associated with each script and orientation, respectively. Also, the substantially matching script and orientation are declared as the script and orientation of the image, if the computed concatenated VDV and HDV of the image substantially match with the reference VDV and HDV of the matching script and orientation, respectively.

Abstract: A system and method for script and orientation detection of images using artificial neural networks (ANNs) are disclosed. In one example, textual content in the image is extracted. Further, a vertical component run (VCR) and horizontal component run (HCR) are obtained by vectorizing each connected component in the extracted textual content. Furthermore, a zonal density run (ZDR) is obtained for each connected component in the extracted textual content. In addition, a concatenated vertical document vector (VDV), horizontal document vector (HDV), and zonal density vector (ZDV) is computed by normalizing the obtained VCR, HCR, and ZDR, respectively, for each connected component. Moreover, the script in the image is determined using a script detection ANN module and the concatenated VDV, HDV, and ZDV of the image. Also, the orientation of the image is determined using an orientation detection ANN module and the concatenated VDV, HDV, and ZDV of the image.

Abstract: A system and method for script and orientation detection of images are disclosed. In one example, textual content in the image is extracted. Further, a vertical component run (VCR) and horizontal component run (HCR) are obtained by vectorizing each connected component in the extracted textual content. Furthermore, a concatenated vertical document vectors (VDV) and a horizontal document vector (HDV) are computed. In addition, a substantially matching script and orientation is obtained by comparing the computed concatenated VDV and HDV of the image with reference VDV and HDV associated with each script and orientation, respectively. Also, the substantially matching script and orientation are declared as the script and orientation of the image, if the computed concatenated VDV and HDV of the image substantially match with the reference VDV and HDV of the matching script and orientation, respectively.

Abstract: A system and method for a low complexity and robust spatiotemporal combining for video enhancement is disclosed. In one embodiment, the method includes computing a standard deviation estimate between a video frame and a temporally neighboring frame of the video frame in a video sequence, computing an error value, for each sub-block of pixels within a block of pixels in a current video frame, between pixel values within the sub-block in the current video frame and corresponding motion compensated pixel values in a temporally neighboring video frame of the current video frame, computing a temporal weighting factor for each sub-block of pixels as a function of the error value and the standard deviation estimate, and combining the block of pixels in the current video frame and their corresponding motion compensated pixel values in the temporally neighboring video frame using the computed temporal weighting factor.