A question and answer (Q&A) system is enhanced to support natural language queries into any document format regardless of where the underlying documents are stored. The Q&A system may be implemented “as-a-service,” e.g., a network-accessible information retrieval platform. Preferably, the techniques herein enable a user to quickly and reliably locate a document, page, chart, or data point that he or she is looking for across many different datasets. This provides for a unified view of all of the user's (or, more generally, an enterprise's) information assets (such as Adobe® PDFs, Microsoft® Word documents, Microsoft Excel spreadsheets, Microsoft PowerPoint presentations, Google Docs, scanned materials, etc.), and to be able to deeply search all of these sources for the right document, page, sheet, chart, or even answer to a question.

Methods and systems disclosed herein may quantify the content and nature of a first stream of text to detect when the typical composition of the first stream of text changes. Quantifying the content and nature of the first stream of text may begin by generating a baseline representation of the content of the first stream of text as represented by a first matrix. Once generated, the first matrix may be used as a control against subsequently received sequences of text. In this regard, a second matrix may be generated from a second sequence of text and compared to the first matrix to determine the differences between the first sequence of text and the second sequence of text. Once a difference is determined, the difference may be compared to a threshold value and, when the difference exceeds the threshold value, an administrator may be notified and corrective action taken.

A fingerprint image processing device includes a memory, and a processor coupled to the memory. The processor performs operations. The operations include reading a tenprint card image which includes a plurality of fingerprint patterns and at least one ruled line to separate one fingerprint imprint area from another fingerprint imprint area, and extracting from the tenprint card image a fingerprint image which includes at least one of the fingerprint patterns, apart of a fingerprint imprint area, and a part of a next fingerprint imprint area.

Detecting the start of a credit roll within video program may allow for the automatic extension of video recordings among other functions. The start of the credit roll may be detected by determining the number of text blocks within a sequence of frames and identifying a point in the sequence of frames where a difference between the number of text blocks in frames occurring before the point and the number of text blocks in frames occurring after the point is greatest and exceeds a specified threshold. Text blocks may be identified within each frame by partitioning the frame into one or more segments and recording the segments having a pixel of a sufficiently high contrast. Contiguous segments may be merged or combined into single blocks, which may then be filtered to remove noise and false positives. Additional content may be inserted into the credit roll frames.

A surgical system includes a detector that includes an array of pixels configured to detect light reflected by a surgical device and generate a first signal. The first signal includes a first dataset representative of a visible image of the surgical device. The surgical system also includes a processor configured to receive the first signal and a second signal representative of one or more operating parameters of the surgical device. The processor is also configured to generate a modified image of the surgical device that includes information related to one or more operating parameters.

Disclosed in some examples are methods, systems, devices, and machine-readable mediums that provide an ability for an entity to independently commence, advance, and complete a resource allocation offer in a matter of minutes as opposed to weeks or months after an automated resource pre-committal process. The system, using and incorporating machine learning techniques and algorithms, may have several phases, including a setup phase, resource pre-committal phase, an import phase, a processing phase, a verification phase, a resource allocation offer phase, and a resource allocation phase in which the system allocates resources to a vendor.

Disclosed are a sensing method and apparatus, according to one embodiment, which process distance information about a feature point by capturing a pattern image. Specifically, disclosed are a sensing method and apparatus capable of improving the quantity and/or accuracy of information to be sensed, by appropriately realizing repeating shapes of a pattern image.

An auto-rotation module having a single-layer neural network on a user device can convert a document image to a monochrome image having black and white pixels and segment the monochrome image into bounding boxes, each bounding box defining a connected segment of black pixels in the monochrome image. The auto-rotation module can determine textual snippets from the bounding boxes and prepare them into input images for the single-layer neural network. The single-layer neural network is trained to process each input image, recognize a correct orientation, and output a set of results for each input image. Each result indicates a probability associated with a particular orientation. The auto-rotation module can examine the results, determine what degree of rotation is needed to achieve a correct orientation of the document image, and automatically rotate the document image by the degree of rotation needed to achieve the correct orientation of the document image.

Systems and methods for determining a dominant color in a digital image are provided, including a computing device configured to analyze pixels in a first pixel group of a digital image based on a first sample rate, analyze pixels in a second pixel group of the digital image based on a second sample rate, and determine a dominant color for the digital image based on the analyzed pixels in the first pixel group and the analyzed pixels in the second pixel group. The pixels in the first pixel group are closer to a center of the digital image than the pixels in the second pixel group and the first sample rate is greater than the second sample rate.

A method of identifying and recognizing characters using a dual-stage neural network pipeline, the method including: receiving, by a computing device, image data; providing the image data to a first convolutional layer of a convolutional neural network (CNN); applying, using the CNN, pattern recognition to the image data to identify a region of the image data containing text; providing sub-image data comprising the identified region of the image data to a convolutional recurrent neural network (CRNN); and recognizing, using the CRNN, the characters within the sub-image data.