Abstract: An integrated circuit is provided and includes a multi-bit cell having multiple bit cells disposed in multiple cell rows. The bit cells include M bit cells, M being positive integers. A first bit cell of the bit cells and a M-th bit cell of the bit cells are arranged diagonally in different cell rows in the multi-bit cell. The multi-bit cell includes first to fourth cell boundaries. The first and second boundaries extend in a first direction and the third and fourth boundaries extend in a second direction different from the first direction. The first bit cell and a second bit cell of the bit cells abut the third cell boundary, and the first bit cell and a (M/2+1)-th bit cell of the bit cells abut the first cell boundary.

Abstract: Semiconductor devices and methods of forming the same are provided. In one embodiment, a semiconductor device includes an active region including a channel region and a source/drain region and extending along a first direction, and a source/drain contact structure over the source/drain region. The source/drain contact structure includes a base portion extending lengthwise along a second direction perpendicular to the first direction, and a via portion over the base portion. The via portion tapers away from the base portion.

Abstract: The present disclosure provides systems and methods for text entry through handwritten shorthand stroke patterns. One example computer-implemented method includes receiving, by a mobile computing device, data descriptive of an input stroke pattern entered by a user. The input stroke pattern includes one or more strokes that approximate a non-linguistic symbol. The method includes identifying, by the mobile computing devices, one of a plurality of shorthand stroke patterns as a matched shorthand pattern to which the input stroke pattern corresponds. The plurality of shorthand stroke patterns have been previously defined by the user. A plurality of output text strings are respectively associated with the plurality of shorthand stroke patterns. The method further includes, in response to identifying the matched shorthand pattern, entering, by the mobile computing device, the output text string associated with the matched shorthand pattern into a text entry field.

Abstract: Implementations of the disclosed subject matter provide methods and systems for identifying a candidate character cut for an overwritten character. A method may include providing a handwriting input area. The handwriting input area may be divided into multiple sections and a first portion of the multiple sections may be located in an end point region. A first handwritten input comprising a first stroke that ends in a section located in the end point region may be received. A second handwritten input comprising a second stroke that begins in a section that is not located in the end point region may be received. As a result, a first candidate character cut may be identified between the first stroke and the second stroke.

Abstract: Techniques are provided for segmenting an input by cut point classification and training a cut classifier. A method may include receiving, by a computerized text recognition system, an input in a script. A heuristic may be applied to the input to insert multiple cut points. For each of the cut points, a probability may be generated and the probability may indicate a likelihood that the cut point is correct. Multiple segments of the input may be selected, and the segments may be defined by cut points having a probability over a threshold. Next, the segments of the input may be provided to a character recognizer. Additionally, a method may include training a cut classifier using a machine learning technique, based on multiple text training examples, to determine the correctness of a cut point in an input.

Abstract: Techniques are provided for segmenting an input by cut point classification and training a cut classifier. A method may include receiving, by a computerized text recognition system, an input in a script. A heuristic may be applied to the input to insert multiple cut points. For each of the cut points, a probability may be generated and the probability may indicate a likelihood that the cut point is correct. Multiple segments of the input may be selected, and the segments may be defined by cut points having a probability over a threshold. Next, the segments of the input may be provided to a character recognizer. Additionally, a method may include training a cut classifier using a machine learning technique, based on multiple text training examples, to determine the correctness of a cut point in an input.

Abstract: Implementations of the disclosed subject matter provide methods and systems for identifying a candidate character cut for an overwritten character. A method may include providing a handwriting input area. The handwriting input area may be divided into multiple sections and a first portion of the multiple sections may be located in an end point region. A first handwritten input comprising a first stroke that ends in a section located in the end point region may be received. A second handwritten input comprising a second stroke that begins in a section that is not located in the end point region may be received. As a result, a first candidate character cut may be identified between the first stroke and the second stroke.