Techniques are described for extracting key values from a document without having to rely on finding corresponding labels for the target keys within the extracted text of the document. Further the techniques do not rely on knowledge of the correlation between (a) the location of labels within a document, and (b) the location of the key values that correspond to the labels. Key values are extracted from a document by, identifying candidate values within the document, establishing “joint-candidate” sets from those candidate values, and using a trained machine learning mechanism to score each joint-candidate set of values. The highest scoring joint-candidate set is deemed to reflect the correct mapping of candidate values to target keys for the document.

An information processing apparatus includes a processor configured to acquire a first recognition result and a first recognition probability on target data from a first recognizer, acquire a second recognition result and a second recognition probability on the target data from a second recognizer, execute checking of the first recognition result and the second recognition result, and execute first control in a case where the first recognition result and the second recognition result match each other as a result of the checking. The first control is control for executing either of first processing or second processing on the matched recognition result and outputting a processing result based on at least one of the first recognition probability or the second recognition probability. A human workload for the first processing is smaller than a human workload for the second processing.

A document processing framework (DPF) for robotic process automation (RPA) is provided. The DPF may allow plug-and-play use of different vendor products on same platform, where users can setup a basic schema for document processing and document understanding workflow. The DPF may allow users to define a taxonomy, digitize a file, classify the file into one or more document types, validate the classification, extract data, validate the extracted data, train classifiers, and/or train extractors. A public package may be provided that can be used by software developers to manage the DPF and build their own classifier and extractor components.

A vision analytics and validation (VAV) system for providing an improved inspection of robotic assembly, the VAV system comprising a trained neural network three-way classifier, to classify each component as good, bad, or do not know, and an operator station configured to enable an operator to review an output of the trained neural network, and to determine whether a board including one or more “bad” or a “do not know” classified components passes review and is classified as good, or fails review and is classified as bad. In one embodiment, a retraining trigger to utilize the output of the operator station to train the trained neural network, based on the determination received from the operator station.

Techniques for providing improved optical character recognition (OCR) for receipts are discussed herein. Some embodiments may provide for a system including one or more servers configured to perform receipt image cleanup, logo identification, and text extraction. The image cleanup may include transforming image data of the receipt by using image parameters values that optimize the logo identification, and performing logo identification using a comparison of the image data with training logos associated with merchants. When a merchant is identified, a second image clean up may be performed by using image parameter values optimized for text extraction. A receipt structure may be used to categorize the extracted text. Improved OCR accuracy is also achieved by applying on format rules of the receipt structure to the extracted text.

Methods, systems, and apparatus for efficient image analysis. In some aspects, a system includes a camera configured to capture images, one or more environment sensors configured to detect movement of the camera, a data processing apparatus, and a memory storage apparatus in data communication with the data processing apparatus. The data processing apparatus can access, for each of a multitude of images captured by a mobile device camera, data indicative of movement of the camera at a time at which the camera captured the image. The data processing apparatus can also select, from the images, a particular image for analysis based on the data indicative of the movement of the camera for each image, analyze the particular image to recognize one or more objects depicted in the particular image, and present content related to the one or more recognized objects.

An electronic device according to various embodiments includes a communication circuit, a memory, and a processor, and the processor is configured to: receive a first image from a first external electronic device by using the communication circuit; perform image recognition with respect to the first image by using the first image; generate information regarding an external object included in the first image, based on a result of the recognition; based on the information regarding the external object satisfying a first designated condition, transmit at least a portion of the first image to a second external electronic device corresponding to the first designated condition; and, based on the information regarding the external object satisfying a second designated condition, transmit the at least portion of the first image to a third external electronic device corresponding to the second designated condition.

Methods, systems, and apparatus for efficient image analysis. In some aspects, a system includes a camera configured to capture images, one or more environment sensors configured to detect movement of the camera, a data processing apparatus, and a memory storage apparatus in data communication with the data processing apparatus. The data processing apparatus can access, for each of a multitude of images captured by a mobile device camera, data indicative of movement of the camera at a time at which the camera captured the image. The data processing apparatus can also select, from the images, a particular image for analysis based on the data indicative of the movement of the camera for each image, analyze the particular image to recognize one or more objects depicted in the particular image, and present content related to the one or more recognized objects.

A computing system for learning a device type and message formats used by a device is provided. The computing system includes an interface and a processor. The interface is receptive of documents describing identification information and communication and application protocols of devices. The processor is coupled with the interface to obtain rules of network packet analysis using document analytics and identify identification information and communication and application protocols of network messages from devices using the rules.

Techniques for providing improved optical character recognition (OCR) for receipts are discussed herein. Some embodiments may provide for a system including one or more servers configured to perform receipt image cleanup, logo identification, and text extraction. The image cleanup may include transforming image data of the receipt by using image parameters values that optimize the logo identification, and performing logo identification using a comparison of the image data with training logos associated with merchants. When a merchant is identified, a second image clean up may be performed by using image parameter values optimized for text extraction. A receipt structure may be used to categorize the extracted text. Improved OCR accuracy is also achieved by applying on format rules of the receipt structure to the extracted text.