A system for generating textual instructions for manufacturers from hybrid textual and image data includes a manufacturing instruction generator that may generate a language processing module from a first training set including at least a training annotated file describing at least a first product to manufacture, the at least an annotated file containing one or more textual data, and at least an instruction set containing one or more manufacturing instructions to manufacture the at least a first product. Manufacturing instruction generator may use the language processing to generate textual instructions for manufacturers from at least an annotated file and may initiate manufacture using the generated manufacturing instructions.

Systems, methods and computer program products for detecting objects using a multi-detector are disclosed, according to various embodiments. In one aspect, a computer-implemented method includes defining an analysis profile comprising an initial number of analysis cycles dedicated to each of a plurality of detectors, where each detector is independently configured to detect objects according to a unique set of analysis parameters and/or a unique detector algorithm. The method also includes: receiving digital video data that depicts at least one object; analyzing the digital video data using some or all of the detectors in accordance with the analysis profile, where the analyzing produces an analysis result for each detector used in the analysis. Further, the method includes updating the analysis profile by adjusting the number of analysis cycles dedicated to at least one of the detectors based on the analysis results.

Described embodiments relate to a method comprising: determining a candidate document comprising image data and character data and extracting the image data and the character data from the candidate document. The method comprises providing, to an image-based numerical representation generation model, the image data, and generating, by the image-based numerical representation generation model, an image-based numerical representation of the image data. The method comprises providing, to a character-based numerical representation generation model, the character data; and generating, by the character-based numerical representation generation model, a character-based numerical representation of the character data. The method comprises providing, to a consolidated image-character based numerical representation generation model, the image-based numerical representation and the character-based numerical representation; and generating, by the consolidated image-character based numerical representation generation model, a combined image-character based numerical representation of the candidate document.

Described embodiments relate to a method comprising: determining a candidate document comprising image data and character data and extracting the image data and the character data from the candidate document. The method comprises providing, to an image-based numerical representation generation model, the image data, and generating, by the image-based numerical representation generation model, an image-based numerical representation of the image data. The method comprises providing, to a character-based numerical representation generation model, the character data; and generating, by the character-based numerical representation generation model, a character-based numerical representation of the character data. The method comprises providing, to a consolidated image-character based numerical representation generation model, the image-based numerical representation and the character-based numerical representation; and generating, by the consolidated image-character based numerical representation generation model, a combined image-character based numerical representation of the candidate document.

A method and system for inserting hand-written text is disclosed. The method includes detecting, from a stylus, an insertion gesture on a touch screen, determining, on the touch screen, an insertion location where the hand-written text is to be inserted, generating, on the touch screen, an insertion box for receiving the hand-written text from the stylus, detecting, from the stylus, the hand-written text in the insertion box, and, in response to determining that the hand-written text nears or exceeds a boundary of the insertion box, increasing a size of the insertion box to accommodate the hand-written text. The method further includes detecting, from the stylus, a completion gesture on the touch screen, reducing the size of the insertion box to encapsulate the inserted hand-written text, and erasing the insertion box and inserting the hand-written text into a space previously occupied by the insertion box.

An ink data modification or correction method, and an information processing device and a program for implementing the method are provided, which allow automatic correction of ink data including a spelling error in a handwritten character string. An ink data modification method according to the present disclosure includes determining a modification method of ink data by detecting a spelling error included in a handwritten character string represented by the ink data, and modifying the ink data by manipulating the ink data on the basis of the determined modification method. For example, the determined modification method may be to add a missing character, or to delete a superfluous character, or to correct a typo by replacing an erroneous character with a correct character.

The present disclosure provides a training method of a text recognition model, a text recognition method, and an apparatus, relating to the technical field of artificial intelligence, and specifically, to the technical field of deep learning and computer vision, which can be applied in scenarios such as optional character recognition, etc. The specific implementation solution is: performing mask prediction on visual features of an acquired sample image, to obtain a predicted visual feature; performing mask prediction on semantic features of acquired sample text, to obtain a predicted semantic feature, where the sample image includes text; determining a first loss value of the text of the sample image according to the predicted visual feature; determining a second loss value of the sample text according to the predicted semantic feature; training, according to the first loss value and the second loss value, to obtain the text recognition model.

The present disclosure provides a training method of a text recognition model, a text recognition method, and an apparatus, relating to the technical field of artificial intelligence, and specifically, to the technical field of deep learning and computer vision, which can be applied in scenarios such as optional character recognition, etc. The specific implementation solution is: performing mask prediction on visual features of an acquired sample image, to obtain a predicted visual feature; performing mask prediction on semantic features of acquired sample text, to obtain a predicted semantic feature, where the sample image includes text; determining a first loss value of the text of the sample image according to the predicted visual feature; determining a second loss value of the sample text according to the predicted semantic feature; training, according to the first loss value and the second loss value, to obtain the text recognition model.

An information processing system includes circuitry configured to accept a selection of specification information from a list of the specification information displayed on a display, the specification information being included in form information acquired by performing form recognition; and display, on the display, an input field in which journal information based on the selected specification information is input.

A method is provided that includes receiving, from a first application, a first image file and a first set of metadata associated with the first image file, and updating an on-device index based on the first image file and the first set of metadata. The method may further include processing the first image file and the first set of metadata to generate a second set of metadata and updating the on-device index based on the second set of metadata. Upon receiving an image search query the process may identify a plurality of candidate image files from the on-device index based on the image search query, and providing the plurality of candidate image files for display in response to the image search query.