A method includes: obtaining an image of a keyboard layout; detecting, from the image, a plurality of key boundaries; determining a label string for a portion of the image defined by a corresponding key boundary from the plurality of key boundaries; selecting, for the determined label string, a corresponding key action; generating a keyboard layout definition for the image of the keyboard layout, the keyboard layout definition including a key definition for the corresponding key boundary, the key definition having: (i) a position for rendering of a key, (ii) a label configured to be rendered at the position of the key, and (iii) an action configured to be caused by selection of the key; and communicating the keyboard layout definition for deployment to a mobile device.

Methods, systems, and apparatus are provided for detecting and extracting data from a nested table. A contour image of a first table disposed within a cell of a second table from an input image is identified. The first table on the input image is masked to generate a masked image of the second table that omits the first table. Cells of the first table and the second table are detected by identifying a plurality of regions of interest (ROIs), determining a row of a plurality of cells based on a common y-coordinate among of the plurality of ROIs, and generating cell parameter information for each of the plurality of cells that identifies the y-coordinate, a size, and a relative position of each determined cell. An output file is generated based on the cell parameter information of the plurality of cells.

One or more embodiments of the present disclosure provide a method for recognizing a three-dimensional (3D) character. The method includes obtaining depth information of the 3D character, wherein the depth information of the 3D character may be depth information used to indicate position information; recognizing the 3D character based on the depth information. A recognition result of the recognizing the 3D character may include a two-dimensional image corresponding to the 3D character and/or a character meaning corresponding to the 3D character.

A system, method and computer program product for use in managing collaboration on documents having digital ink on a network of computing devices is disclosed. Each computing device has a processor and at least one system application for processing handwriting input under control of the processor. The system application displays, on a display associated with one of the computing devices, a document having digital ink based on a journal of the document, defines the journal to have journal entries associated with at least handwriting input to the document represented by the digital ink, and communicates the journal entries of the journal with one or more of the other networked computing devices displaying the document. The handwriting input associated with the journal entries is handwriting input to the document via the input interface of any of the computing devices displaying the document based on the communicated journal entries.

An image processing system performs tilt correction with respect to a document image having handwritten characters and typed letters mixed with each other. The image processing system separates the document image into an image with handwritten characters determined as handwritten characters and an image without handwritten characters not determined as handwritten characters, estimates a tilt angle of the image without handwritten characters, and corrects the document image on the basis of the tilt angle.

One embodiment provides a method comprising receiving content for presentation on a display, and obtaining a plurality of image patches from the content. Each image patch is segmented from a bottom and horizontal-center portion of a sub-sampled grayscale image of a frame of the content. The method further comprises applying a searching algorithm to the plurality of image patches to detect a region of interest of moving text in the content. The searching algorithm utilizes a first set of features and a second set of features to detect the region of interest with respect to a vertical axis and a horizontal axis of the display, respectively. The method further comprises correcting one or more visual artifacts in the region of interest, where the one or more visual artifacts include broken text.

A system is discussed herein that is configured for extracting data from documents. In particular, the system may be utilized for automating and computerized checking of transit and shipping related documents. For example, the documents may include various data, such delivery dates, prices, inventory identification, personnel identification, container identification, customs documents, transport documents, a combination thereof, and the like.

Systems and methods for detecting stamps include if at least one regular shape in an image of a document is detected then outputting said at least one regular shape as a stamp; else: removing at least one of text, lines, and noise in the image of the document; and if at least one closed shape is remaining in the image of the document, then inscribing said at least one closed shape and outputting said at least one inscribed closed shape as a stamp; else if at least one open shape is remaining in the image of the document, then enclosing said at least one open shape and outputting said at least one enclosed shape as a stamp.

A system is discussed herein that is configured for extracting data from documents. In particular, the system may be utilized for automating and computerized checking of transit and shipping related documents. For example, the documents may include various data, such delivery dates, prices, inventory identification, personnel identification, container identification, customs documents, transport documents, a combination thereof, and the like.

Disclosed are various embodiments for intelligent text recognition based upon a selected pattern detection mode. First, text can be identified in an image. A pattern detection mode can be selected by a user or autonomously. In some instances, the pattern detection mode can be selected based at least in part on a user account. Next, the text can be parsed for occurrences of a pattern associated with the selected pattern detection mode. A list of occurrences of the pattern can be generated from the text and presented to a user. In some instances, a user can train a model to learn a new pattern.