Abstract: Metrology methods, modules and systems are provided, for using machine learning algorithms to improve the metrology accuracy and the overall process throughput. Methods comprise calculating training data concerning metrology metric(s) from initial metrology measurements, applying machine learning algorithm(s) to the calculated training data to derive an estimation model of the metrology metric(s), deriving measurement data from images of sites on received wafers, and using the estimation model to provide estimations of the metrology metric(s) with respect to the measurement data. While the training data may use two images per site, in operation a single image per site may suffice—reducing the measurement time to less than half the current measurement time. Moreover, confidence score(s) may be derived as an additional metrology and process control, and deep learning may be used to enhance the accuracy and/or speed of the metrology module.

Abstract: Metrology methods, modules and systems are provided, for using machine learning algorithms to improve the metrology accuracy and the overall process throughput. Methods comprise calculating training data concerning metrology metric(s) from initial metrology measurements, applying machine learning algorithm(s) to the calculated training data to derive an estimation model of the metrology metric(s), deriving measurement data from images of sites on received wafers, and using the estimation model to provide estimations of the metrology metric(s) with respect to the measurement data. While the training data may use two images per site, in operation a single image per site may suffice—reducing the measurement time to less than half the current measurement time. Moreover, confidence score(s) may be derived as an additional metrology and process control, and deep learning may be used to enhance the accuracy and/or speed of the metrology module.

Abstract: Metrology methods, modules and systems are provided, for using machine learning algorithms to improve the metrology accuracy and the overall process throughput. Methods comprise calculating training data concerning metrology metric(s) from initial metrology measurements, applying machine learning algorithm(s) to the calculated training data to derive an estimation model of the metrology metric(s), deriving measurement data from images of sites on received wafers, and using the estimation model to provide estimations of the metrology metric(s) with respect to the measurement data. While the training data may use two images per site, in operation a single image per site may suffice—reducing the measurement time to less than half the current measurement time. Moreover, confidence score(s) may be derived as an additional metrology and process control, and deep learning may be used to enhance the accuracy and/or speed of the metrology module.

Abstract: Metrology methods, modules and systems are provided, for using machine learning algorithms to improve the metrology accuracy and the overall process throughput. Methods comprise calculating training data concerning metrology metric(s) from initial metrology measurements, applying machine learning algorithm(s) to the calculated training data to derive an estimation model of the metrology metric(s), deriving measurement data from images of sites on received wafers, and using the estimation model to provide estimations of the metrology metric(s) with respect to the measurement data. While the training data may use two images per site, in operation a single image per site may suffice—reducing the measurement time to less than half the current measurement time. Moreover, confidence score(s) may be derived as an additional metrology and process control, and deep learning may be used to enhance the accuracy and/or speed of the metrology module.

Abstract: A computer implemented method, a computerized system and a computer program product for automatic image segmentation. The computer implemented method comprises obtaining an image of a tissue, wherein the image is produced using an imaging modality. The method further comprises automatically identifying, by a processor, a tissue segment within the image, wherein said identifying comprises identifying an artifact within the image, wherein the artifact is a misrepresentation of a tissue structure, wherein the misrepresentation is associated with the imaging modality; and searching for the tissue segment in a location adjacent to the artifact.

Abstract: A computer implemented method, a computerized system and a computer program product for automatic image segmentation. The computer implemented method comprises obtaining an image of a tissue, wherein the image is produced using an imaging modality. The method further comprises automatically identifying, by a processor, a tissue segment within the image, wherein said identifying comprises identifying an artifact within the image, wherein the artifact is a misrepresentation of a tissue structure, wherein the misrepresentation is associated with the imaging modality; and searching for the tissue segment in a location adjacent to the artifact.

Abstract: A method for optical character recognition (OCR) verification, the method includes: receiving a first character image that was obtained from applying an OCR process on a document; wherein the first character image is classified, by the OCR, as being associated with a first character; receiving a first character code of a text; replacing the first character code by the first character image; and evaluating a correctness of the OCR based upon a response of a user to a display of the text first character image.

Abstract: A method for optical character recognition (OCR) verification, the method includes: receiving a first character image that was obtained from applying an OCR process on a document; wherein the first character image is classified, by the OCR, as being associated with a first character; receiving a first character code of a text; replacing the first character code by the first character image; and evaluating a correctness of the OCR based upon a response of a user to a display of the text first character image.

Abstract: Text is extracted from a grayscale or color compound digital image. Kernels of text in the compound digital image are found using a stroke operator. The kernels of text are segmented into text blocks based on image space, color space, and intensity space. Each text block is segmented into text and background pixels using active contour analysis. The segmented text blocks are refined by altering parameters in the active contour analysis. Text is extracted from the refined segmented text blocks, and a binary image is created including text extracted from the refined segmented text blocks.

Abstract: A method for providing recognition error correction information, the method includes: obtaining metadata associated with a capture of a media item; and generating recognition error correction information in response to the metadata. The recognition error correction information is to be used in a recognition process selected out of a list consisting of an automatic speech recognition process and an optical characters recognition process.