Electronic handwriting processor with convolutional neural networks
AssigneePearson Education, Inc.
InventorZhaodong Wang
Life of this patent
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Abstract
An improved machine learning system is provided. For example, a content management server may provide real-time analysis of a user's handwriting to assess the user's knowledge of a language, including using a convolution neural network method. The convolution neural network method may be executed to normalize at least some identified strokes in the user's handwritten user input. Normalization may be performed by translating a window comprising a subset of pixels in a digital representation of the handwritten user input amongst a plurality of pixels in the digital representation.
Claims15
A computer-implemented method for automated handwriting assessment comprising:
- generating a digital representation of a handwritten user input;
- identifying strokes in the digital representation of the handwritten user input;
- storing the identified strokes;
- executing a heuristic segmentation method to correct a slant of at least some of the identified strokes using linear regression;
- executing a convolution neural network model to normalize the at least some of the identified strokes by performing a convolution by translating a window comprising a subset of pixels in the digital representation of the handwritten user input amongst a plurality of pixels in the digital representation;
- segmenting the digital representation of the handwritten user input into normalized digital strokes;
- determining a number of characters in a correct response word;
- determining at least one character associated with the normalized digital strokes, wherein the at least one character includes a number of characters equal to the number of characters in the correct response word;
- and
- generating a word hypothesis by applying a model to the at least one character.
A computer device including a content management system for improving distribution and conversion of academic data, the content management system comprising:
- one or more processors;
- and
- one or more memories coupled with the one or more processors, wherein the one or more processors and the one or more memories are configured to:
- generate a digital representation of a handwritten user input;
- identify strokes in the digital representation of the handwritten user input;
- store the identified strokes;
- execute a heuristic segmentation method to correct a slant of at least some of the identified strokes using linear regression;
- execute a convolution neural network model to normalize the at least some of the identified strokes by performing a convolution by translating a window comprising a subset of pixels in the digital representation of the handwritten user input amongst a plurality of pixels in the digital representation;
- segment the digital representation of the handwritten user input into normalized digital strokes;
- determine a number of characters in a correct response word;
- determine at least one character associated with the normalized digital strokes, wherein the at least one character includes a number of characters equal to the number of characters in the correct response word;
- and
- generate a word hypothesis by applying a model to the at least one character.
One or more non-transitory computer-readable storage media device collectively storing computer-executable instructions that, when executed by one or more computer systems, configure the one or more computer systems to collectively perform operations comprising:
- generating a digital representation of a handwritten user input;
- identifying strokes in the digital representation of the handwritten user input;
- storing the identified strokes;
- executing a heuristic segmentation method to correct a slant of at least some of the identified strokes using linear regression;
- executing a convolution neural network model to normalize the at least some of the identified strokes by performing a convolution by translating a window comprising a subset of pixels in the digital representation of the handwritten user input amongst a plurality of pixels in the digital representation;
- segmenting the digital representation of the handwritten user input into normalized digital strokes;
- determining a number of characters in a correct response word;
- determining at least one character associated with the normalized digital strokes, wherein the at least one character includes a number of characters equal to the number of characters in the correct response word;
- and
- generating a word hypothesis by applying a model to the at least one character.
Dependencies are inferred from the claim text.