A method for authenticating an object, comprising determining a physical dispersion pattern of a set of elements, determining a physical characteristic of the set of elements which is distinct from a physical characteristic producible by a transfer printing technology, determining a digital code associated with the object defining the physical dispersion pattern, and authenticating the object by verifying a correspondence of the digital code with the physical dispersion pattern, and verifying the physical characteristic.

A method and system identifies and addresses imaging artifacts in an image of a financial document to enable a software system to provide financial services based on the image of the financial document. The method and system receive document image data, extract image features from the document image data, and apply the image features to an analytics model to generate an image classification, according to one embodiment. The method and system use the image classification to determine whether the document image data contains one or more particular image artifacts (e.g., Moiré patterns), according to one embodiment. If the software system determines that it is likely that the document image data contains one or more particular image artifacts, then the software system applies a filter (e.g., a median filter) to the document image data to reduce and/or remove the one or more particular image artifacts, according to one embodiment.

Provided is a system for identifying a drug container that includes an image capture device configured to capture an optical image of a portion of a label provided to the drug container, and a non-transitory computer storage medium storing a plurality of features for each of a plurality of different drugs included in a database. The features associated with each of the plurality of different drugs are suggestive of an identity of a drug identity but do not, alone, positively identify the drug identity. A processing component is configured to receive data representing the optical image transmitted by the image capture device and to identify a candidate for the drug identity based on the plurality of features associated with the candidate.

Digital signature authentication systems and methods are disclosed. An example method of authenticating a digital signature extracted from a high resolution image includes comparing an extracted signature from an image to a reference signature. The example method also includes authenticating the extracted signature against the reference signature according to one of a plurality of different authentication protocols.

An information processing apparatus includes a recognition unit. The recognition unit has image recognition methods of plural types. The recognition unit recognizes, in a case where image data of a first document and image data of a second document are generated by a generation unit that reads a document and generates image data of the document, the type of the first document from the image data of the first document and recognizing the type of the second document from the image data of the second document using an image recognition method corresponding to the type of the first document among the image recognition methods of the plural types, the first document and the second document being included in plural documents.

An image processing apparatus includes an acquisition section, a marker detecting section, a text extracting section, and a markup language processing section. The marker detecting section detects, based on the image data acquired by the acquisition section, a marker assigned to an original document. The text extracting section analyzes the image data to recognize and extract a text in the original document. The markup language processing section generates markup data in which the text in the image data has the same display color as the marker.

Disclosed are devices and methods for processing an image document in a client-server environment such that privacy of text information contained in the image document is preserved. Specifically, in a client-server environment, an image document can be processed using a local computerized device of a client to create an obfuscated document by identifying word images in the image document and scrambling those word images. The obfuscated document can be received by a server of a service provider over a network (e.g., the Internet) and processed by previously trained software (e.g., a previously trained convolutional neural network (CNN)) to recognize specific words represented by the scrambled images in the obfuscated document without having to reconstruct the image document. Since the image document is neither communicated over the network, nor reconstructed and stored on the server, privacy concerns are minimized.

Techniques for removing artifacts, such as shadows, from document images are described. A shadow map is generated for a digital image by first determining local background colors using clusters of local pixel intensities. Then, a global reference background color is selected from all pixel intensities of the digital image. Next, a per-pixel scaling factor is determined that maps the local background colors to the global reference background color, which applies localized adjustment to the digital image to remove local shadow.

Disclosed are various embodiments for digitally restoring content from a torn document. A computing device obtains an image of a document that has a tear. The computing device identifies portions of the image separated by a gap region representing the tear in the document. Content in the portions of the image adjacent to the gap region is then identified. The position of a portion of the image is incrementally adjusted to reduce the gap region without overlapping the portions. The position of the portion is adjusted to align the content along a boundary between the portions of the image.

An image processing apparatus includes an input section for inputting image data, and an image processing section for discriminating a marking area out of image data and generating image data of fill-in-blank questions with the marking area converted to a blank answer field. For generation of the image data of fill-in-blank questions, the image processing section recognizes a character count of characters present in the marking area, determines, as an answer-field character count, a character count resulting from adding a margin number to the character count of the marking area, and changes a size of the answer field to a size adapted to the answer-field character count.