Abstract: An image is displayed on a touch screen. A user's underline gesture on the displayed image is detected. The area of the image touched by the underline gesture and a surrounding region approximate to the touched area are identified. Skew for text in the surrounding region is determined and compensated. A text region including the text is identified in the surrounding region and cropped from the image. The cropped image is transmitted to an optical character recognition (OCR) engine, which processes the cropped image and returns OCR'ed text. The OCR'ed text is outputted.

Abstract: An image is displayed on a touch screen. A user's underline gesture on the displayed image is detected. The area of the image touched by the underline gesture and a surrounding region approximate to the touched area are identified. Skew for text in the surrounding region is determined and compensated. A text region including the text is identified in the surrounding region and cropped from the image. The cropped image is transmitted to an optical character recognition (OCR) engine, which processes the cropped image and returns OCR'ed text. The OCR'ed text is outputted.

Abstract: An image is displayed on a touch screen. A user's underline gesture on the displayed image is detected. The area of the image touched by the underline gesture and a surrounding region approximate to the touched area are identified. Skew for text in the surrounding region is determined and compensated. A text region including the text is identified in the surrounding region and cropped from the image. The cropped image is transmitted to an optical character recognition (OCR) engine, which processes the cropped image and returns OCR'ed text. The OCR'ed text is outputted.

Abstract: A live video stream captured by an on-device camera is displayed on a screen with an overlaid guideline. Video frames of the live video stream are analyzed for a video frame with acceptable quality. A text region is identified in the video frame approximate to the on-screen guideline and cropped from the video frame. The cropped image is transmitted to an optical character recognition (OCR) engine, which processes the cropped image and generates text in an editable symbolic form (the OCR'ed text). A confidence score is determined for the OCR'ed text and compared with a threshold value. If the confidence score exceeds the threshold value, the OCR'ed text is outputted.

Abstract: A live video stream captured by an on-device camera is displayed on a screen with an overlaid guideline. Video frames of the live video stream are analyzed for a video frame with acceptable quality. A text region is identified in the video frame approximate to the on-screen guideline and cropped from the video frame. The cropped image is transmitted to an optical character recognition (OCR) engine, which processes the cropped image and generates text in an editable symbolic form (the OCR'ed text). A confidence score is determined for the OCR'ed text and compared with a threshold value. If the confidence score exceeds the threshold value, the OCR'ed text is outputted.

Abstract: An image is displayed on a touch screen. A user's underline gesture on the displayed image is detected. The area of the image touched by the underline gesture and a surrounding region approximate to the touched area are identified. Skew for text in the surrounding region is determined and compensated. A text region including the text is identified in the surrounding region and cropped from the image. The cropped image is transmitted to an optical character recognition (OCR) engine, which processes the cropped image and returns OCR'ed text. The OCR'ed text is outputted.

Abstract: A method for the joint optimization of language model performance and size is presented comprising developing a language model from a tuning set of information, segmenting at least a subset of a received textual corpus and calculating a perplexity value for each segment and refining the language model with one or more segments of the received corpus based, at least in part, on the calculated perplexity value for the one or more segments.

Abstract: A method is presented comprising assigning each of a plurality of segments comprising a received corpus to a node in a data structure denoting dependencies between nodes, and calculating a transitional probability between each of the nodes in the data structure.

Abstract: A method is presented comprising assigning each of a plurality of segments comprising a received corpus to a node in a data structure denoting dependencies between nodes, and calculating a transitional probability between each of the nodes in the data structure.

Abstract: The generation and management of a language model data structure include assigning each segment of a received corpus to a node in a data structure that denotes dependencies between the respective nodes. A transitional probability between each of the nodes in the data structure is calculated. A frequency of occurrence is calculated for each item of the respective segments, and those nodes of the data structure associated with items that do not meet a minimum frequency of occurrence threshold are removed. The data structure may be managed across a system memory of a computer system and an extended memory of the computer system.

Abstract: A method for optimizing a language model is presented comprising developing an initial language model from a lexicon and segmentation derived from a received corpus using a maximum match technique, and iteratively refining the initial language model by dynamically updating the lexicon and re-segmenting the corpus according to statistical principles until a threshold of predictive capability is achieved.

Abstract: A method for optimizing a language model is presented comprising developing an initial language model from a lexicon and segmentation derived from a received corpus using a maximum match technique, and iteratively refining the initial language model by dynamically updating the lexicon and re-segmenting the corpus according to statistical principles until a threshold of predictive capability is achieved.