Abstract: A system and method of providing sender-customization of multi-media messages through the use of emoticons is disclosed. The sender inserts the emoticons into a text message. As an animated face audibly delivers the text, emoticons associated with the message are started a predetermined period of time or number of words prior to the position of the emoticon in the message text and completed a predetermined length of time or number of words following the location of the emoticon. The sender may insert emoticons through the use of emoticon buttons that are icons available for choosing. Upon sender selections of an emoticon, an icon representing the emoticon is inserted into the text at the position of the cursor. Once an emoticon is chosen, the sender may also choose the amplitude for the emoticon and increased or decreased amplitude will be displayed in the icon inserted into the message text.

Abstract: A method is provided for customizing a multi-media message created by a sender for a recipient, in which the multi-media message includes an animated entity audibly presenting speech converted from text by the sender. At least one image is received from the sender. Each of the at least one image is associated with a tag. The sender is presented with options to insert the tag associated with one of the at least one image into the sender text.

Abstract: A system and method of providing sender customization of multi-media messages through the use of inserted images or video. The images or video may be sender-created or predefined and available to the sender via a web server. The method relates to customizing a multi-media message created by a sender for a recipient, the multi-media message having an animated entity audibly presenting speech converted from text created by the sender. The method comprises receiving at least one image from the sender, associating each at least one image with a tag, presenting the sender with options to insert the tag associated with one of the at least one image into the sender text, and after the sender inserts the tag associated with one of the at least one images into the sender text, delivering the multi-media message with the at least one image presented as background to the animated entity according to a position of the tag associated with the at least one image in the sender text.

Abstract: A system and method of providing sender-customization of multi-media messages through the use of emoticons is disclosed. The sender inserts the emoticons into a text message. As an animated face audibly delivers the text, emoticons associated with the message are started a predetermined period of time or number of words prior to the position of the emoticon in the message text and completed a predetermined length of time or number of words following the location of the emoticon. The sender may insert emoticons through the use of emoticon buttons that are icons available for choosing. Upon sender selections of an emoticon, an icon representing the emoticon is inserted into the text at the position of the cursor. Once an emoticon is chosen, the sender may also choose the amplitude for the emoticon and increased or decreased amplitude will be displayed in the icon inserted into the message text.

Abstract: A machine learning paradigm called Graph Transformer Networks extends the applicability of gradient-based learning algorithms to systems composed of modules that take graphs as inputs and produce graphs as output. Training is performed by computing gradients of a global objective function with respect to all the parameters in the system using a kind of back-propagation procedure. A complete check reading system based on these concept is described. The system uses convolutional neural network character recognizers, combined with global training techniques to provides record accuracy on business and personal checks.

Abstract: A method and apparatus extracts a foreground image and a background image from a color document image. The color document image is divided into a plurality of multiscaled grids. Each of the grids includes a plurality of blocks and the resolution of the plurality of blocks increases for each successive grid. The background color and the foreground color of each block of the largest grid is first determined. Then the background color and the foreground color of each block of the smaller grids are determined iteratively, using biasing information from each block's corresponding block, until the background and foreground colors of all of the blocks of all of the grids have been determined. Finally, the foreground and background images are formed from the determined background color and foreground color of each block of the smallest grid.

Abstract: Groups of symbols to be recognized are standardized by fitting four flexible curves to the group of symbols. The curves are fitted by minimizing a cost or energy function that associates a cost with the curvature of the curves, the slant of the curves, the displacement in spacing between the curves and the distance of maxima and minima points from the curves. After the curves are fitted to the group of symbols, the symbols are standardized by transforming coordinates systems so that the fitted curves are placed in a predetermined configuration.

Abstract: Data samples describing a plurality of micro-segments that compose a symbol to be recognized are received from a device such as an electronic pad. A preprocessor maps the micro-segments into cells of an array that has several feature dimensions. The preprocessor assigns values to the cells based on the length of a micro-segment associated with the cell, and how well the features of the associated micro-segment correspond to the feature label of the cell. The cell values are used as inputs to at least one of a plurality of neural networks, where each neural network is trained to identify symbols from a different group of symbols. A sorter examines the symbol to determine which neural network should be used to recognize the symbol. An output produced by the sorter controls a switching means that communicates the cell values to the proper neural network.