公开(公告)号：US10402734B2

公开(公告)日：2019-09-03

申请号：US14836113

申请日：2015-08-26

Applicant: Google Inc.

Inventor： Thomas Deselaers ,  Daniel Martin Keysers ,  Abraham Murray ,  Shumin Zhai

IPC: G06K9/00 ,  G06N5/04 ,  G06F3/0488 ,  G06F3/023

Abstract: A computing device is described that receives first input, at an initial time, of a first textual character and a second input, at a subsequent time, of a second textual character. The computing device determines, based on the first and second textual characters, a first character sequence that does not include a space character between the first and second textual characters and a second character sequence that includes the space character between the first and second textual characters. The computing device determines a first score associated with the first character sequence and a second score associated with the second character sequence. The computing device adjusts, based on a duration of time between the initial and subsequent times, the second score to determine a third score, and responsive to determining that the third score exceeds the first score, the computing device outputs the second character sequence.

公开(公告)号：US20180188938A1

公开(公告)日：2018-07-05

申请号：US15393611

申请日：2016-12-29

Applicant: Google Inc.

Inventor： Thomas Deselaers ,  Victor Carbune

IPC: G06F3/0488 ,  G06F3/044 ,  G06N99/00

CPC classification number: G06F3/04883 ,  G06F3/0418 ,  G06F3/044 ,  G06F3/045 ,  G06N3/02 ,  G06N99/005

Abstract: The present disclosure provides systems and methods that leverage machine learning to predict multiple touch interpretations. In particular, the systems and methods of the present disclosure can include and use a machine-learned touch interpretation prediction model that has been trained to receive touch sensor data indicative of one or more locations of one or more user input objects relative to a touch sensor at one or more times and, in response to receipt of the touch sensor data, provide one or more predicted touch interpretation outputs. Each predicted touch interpretation output corresponds to a different type of predicted touch interpretation based at least in part on the touch sensor data. Predicted touch interpretations can include a set of touch point interpretations, a gesture interpretation, and/or a touch prediction vector for one or more future times.

公开(公告)号：US20170289337A1

公开(公告)日：2017-10-05

申请号：US15090836

申请日：2016-04-05

Applicant: Google Inc.

Inventor： Li-Lun Wang ,  Victor Carbune ,  Dhyanesh Narayanan ,  Henry Rowley ,  Thomas Deselaers

IPC: H04M1/725 ,  G06N3/08 ,  G06F3/0488 ,  G06F3/0484 ,  G06F17/24

CPC classification number: H04M1/72552 ,  G06F3/0484 ,  G06F3/04883 ,  G06F17/242 ,  G06F17/243 ,  G06N3/08 ,  H04M2250/70

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.

公开(公告)号：US20170068868A1

公开(公告)日：2017-03-09

申请号：US14849162

申请日：2015-09-09

Applicant: Google Inc.

Inventor： Victor Carbune ,  Thomas Deselaers ,  Daniel M. Keysers

IPC: G06K9/22 ,  G06F17/27

CPC classification number: G06K9/222 ,  G06F17/275 ,  G06K9/00422 ,  G06K9/6227 ,  G06K9/6807

Abstract: Methods, systems, and devices, including computer programs encoded on a computer storage medium, for improving handwriting detection. In one aspect, a method includes receiving data indicating one or more strokes, determining one or more features of the one or more strokes, determining whether the one or more strokes likely represent a grapheme based at least on one or more of the features, selecting a particular recognition process for processing the data, from among (i) a multi-language recognition process which processes input strokes using multiple recognizers that are each trained to output, for a given set of input strokes, one or more graphemes that are associated with a particular language, and (ii) a single character, universal recognition process which processes input strokes using a universal recognizer that is trained to output, for a given set of input strokes, a single grapheme, and providing the data to the particular recognition process.

Abstract translation: 方法，系统和设备，包括在计算机存储介质上编码的计算机程序，用于改进手写检测。 一方面，一种方法包括接收指示一个或多个笔画的数据，确定一个或多个笔画的一个或多个特征，基于至少一个或多个特征来确定所述一个或多个笔画是否可能代表图形，选择 用于处理数据的特定识别过程，从（i）对于给定的一组输入笔画处理输入笔画的多语言识别处理，所述多语言识别处理处理输入笔画使用多个识别器，所述多个识别器被训练以输出给定的一组输入笔画，所述一个或多个字母与 特定语言，以及（ii）单个字符的通用识别过程，其使用通用识别器来处理输入笔画，所述通用识别器被训练为针对给定的一组输入笔画输出单个字母，并且将数据提供给特定识别过程 。

公开(公告)号：US09286527B2

公开(公告)日：2016-03-15

申请号：US14184997

申请日：2014-02-20

Applicant: Google Inc.

Inventor： Li-Lun Wang ,  Thomas Deselaers ,  Henry Allan Rowley

IPC: G06K9/62 ,  G06K9/00 ,  G06N99/00 ,  G06K9/22 ,  G06K9/66

CPC classification number: G06K9/00865 ,  G06K9/00402 ,  G06K9/222 ,  G06K9/342 ,  G06K9/4604 ,  G06K9/6256 ,  G06K9/66 ,  G06N99/005

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 translation: 提供了通过切点分类对输入进行分割和训练切分分类器的技术。 方法可以包括通过计算机化的文本识别系统接收脚本中的输入。 可以将启发式应用于输入以插入多个切割点。 对于每个切割点，可以产生概率，并且概率可以指示切割点是正确的可能性。 可以选择输入的多个段，并且可以通过具有超过阈值的概率的切点来定义段。 接下来，可以将输入的段提供给字符识别器。 另外，一种方法可以包括基于多个文本训练示例使用机器学习技术来训练切割分类器，以确定输入中的切割点的正确性。

公开(公告)号：US20150169949A1

公开(公告)日：2015-06-18

申请号：US14106893

申请日：2013-12-16

Applicant: GOOGLE INC.

Inventor： Daniel Martin Keysers ,  Thomas Deselaers ,  Henry Allan Rowley

IPC: G06K9/00

CPC classification number: G06K9/00416 ,  G06K9/344 ,  G06K2209/013

Abstract: Methods and systems for recognizing Devanagari script handwriting are provided. A method may include receiving a handwritten input and determining that the handwritten input comprises a shirorekha stroke based on one or more shirorekha detection criteria. Shirorekha detection criteria may be at least one criterion such as a length of the shirorekha stroke, a horizontality of the shirorekha stroke, a straightness of the shirorekha stroke, a position in time at which the shirorekha stroke is made in relation to one or more other strokes in the handwritten input, and the like. Next, one or more recognized characters may be provided corresponding to the handwritten input.

Abstract translation: 提供了识别梵文脚本手写的方法和系统。 方法可以包括接收手写输入并且基于一个或多个shirorekha检测标准确定手写输入包括shirorekha笔划。 Shirorekha检测标准可以是至少一个标准，例如shirorekha中风的长度，shirorekha中风的水平度，shirorekha中风的平直度，相对于一个或多个其他的shirorekha中风的时间位置 手写输入中的笔画等。 接下来，可以对应于手写输入提供一个或多个识别的字符。