# US 7,412,095 B2 · Preprocessing of multi-line rotated electronic ink > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 7,412,095 B2 - Document ID: US7412095B2 - Status: Granted - Kind: B2 - Application number: 11/257,332 - Filed: 2005-10-24 - Published: 2006-02-16 - Granted: 2008-08-12 - Earliest claimed: 2002-05-10 - Assignees: Microsoft Corporation - Inventors: Gregory N. Hullender; Ahmad Abdulkader - Web page: https://patentgrep.com/patent/US7412095B2 ## CPC - G06V 30/1423: {the instrument generating sequences of position coordinates corresponding to handwriting (preprocessing or recognising digital ink G06V30/32)} - G06V 30/1478: {of characters or characters lines} - G06V 30/10: Character recognition - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract An electronic ink object is preprocessed for improving handwriting recognition results. An electronic ink object, possibly having multiple lines of non-horizontal ink, is preprocessed by segmenting or separating the electronic ink object into one or more lines, computing a writing angle of each line, and rotating each line so that it is horizontal. ## Claims ### Claim 1 A method of preprocessing an electronic ink object comprising the steps of: segmenting the electronic ink object into a plurality of lines; estimating a writing angle of each line; and rotating each line to make horizontal; and creating a model of each line for interpretation and further evaluation between the points of local extrema of the segmented electronic ink object and the model to reduce incorrect instances of recognition; wherein the model includes: employing an electronic ink feature generator; employing a line breaking classifier; employing a mapping database; and an ink transformer. ### Claim 2 (depends on 1) The method of preprocessing an electronic ink object of claim 1 further comprising the step of: recognizing the segmented and rotated electronic ink object to identify at least one character. ### Claim 3 (depends on 1) The method of preprocessing an electronic ink object of claim 1 wherein the electronic ink object comprises at least one ink stroke and the step of segmenting includes the step of (i) identifying a plurality of features for each ink stroke of the electronic ink object; and (ii) classifying each ink stroke as part of a new line, a current line, or a previous line. ### Claim 4 (depends on 3) The method of preprocessing an electronic ink object of claim 3 wherein the step of classifying is performed using a neural network. ### Claim 5 (depends on 3) The method of preprocessing an electronic ink object of claim 3 wherein the step of classifying is performed using a hidden Markov model. ### Claim 6 (depends on 3) The method of preprocessing an electronic ink object of claim 3 wherein the step of classifying is performed using a decision tree. ### Claim 7 (depends on 3) The method of preprocessing an electronic ink object of claim 3 wherein the step of classifying is performed using a Bayesian net. ### Claim 8 (depends on 3) The method of preprocessing an electronic ink object of claim 3 wherein the step of classifying is performed using a K Nearest Neighbor (KNN). ### Claim 9 (depends on 3) The method of preprocessing an electronic ink object of claim 3 wherein the step of classifying is performed using an Expert System. ### Claim 10 (depends on 3) The method of preprocessing an electronic ink object of claim 3 wherein if the ink stroke is part of the new line, the step of estimating includes the step of computing a slope of the new line. ### Claim 11 (depends on 3) The method of preprocessing an electronic ink object of claim 3 wherein if the ink stroke is part of the new line, the step of estimating includes the step of computing a slope of a centerline of the new line using a Least Mean Squares algorithm. ### Claim 12 (depends on 1) The method of preprocessing an electronic ink object of claim 1 wherein the step of rotating is performed using a rotation matrix. ### Claim 13 (depends on 1) A computer-readable medium having computer-executable instructions for performing the steps recited in claim 1. ### Claim 14 A system for processing an electronic ink object having at least one ink stroke comprising in combination: an input that receives the electronic ink object; an electronic ink feature generator that receives the inputted electronic ink object and generates at least one feature for the at least one ink stroke of the electronic ink object; a classifier that classifies the at least one ink stroke of the electronic ink object into a plurality of lines based on the feature of the ink stroke; and an ink transformer that rotates each of the plurality of lines of electronic ink corresponding to a center of the ink object from a non-horizontal to a horizontal relationship according to each of the plurality of lines having a spatial orientation deviating from a horizontal placement upon a graphic display and better facilitate preprocessed electronic ink object to written character recognition. ### Claim 15 (depends on 14) The system of claim 14 wherein the classifier is a neural network. ### Claim 16 (depends on 14) The system of claim 14 wherein the classifier is a hidden Markov model. ### Claim 17 (depends on 14) The system of claim 14 wherein the classifier is a decision tree. ### Claim 18 (depends on 14) The system of claim 14 wherein the classifier is a Bayesian net. ### Claim 19 (depends on 14) The system of claim 14 wherein the classifier is a K Nearest Neighbor (KNN). ### Claim 20 (depends on 14) The system of claim 14 wherein the classifier is an Expert System. ### Claim 21 (depends on 14) The system of claim 14 further comprising: a user interface that generates the electronic ink object. ### Claim 22 (depends on 1) The method of preprocessing an electronic ink object of claim 1 wherein the model of each line for interpretation is a Gaussian model. ### Claim 23 (depends on 1) The method of preprocessing an electronic ink object of claim 1 wherein the segmenting the electronic ink object into a plurality of lines based upon at least one occurrence of a cusp within the at least one ink stroke, and defined as differences between tangents on successive points of a chord of the at least one ink stroke. ### Claim 24 (depends on 14) The system of claim 14 wherein the classifier that classifies the at least one ink stroke of the electronic ink object into a plurality of lines based upon at least one occurrence of a cusp within the at least one ink stroke, and defined as differences between tangents on successive points of a chord of the at least one ink stroke.