Integer-Based Graphical Representations of Words and Texts

Abstract

Aspects of the subject disclosure may include, for example, a method of transforming, by a processing system comprising a processor, text comprising a series of characters into a graphic representation, wherein the graphic representation comprises a series of dots arranged in a two-dimensional pattern, wherein the two-dimensional pattern comprises four dots per character, and wherein each dot in the series of dots is one unit away from a preceding dot; and plotting, by the processing system, the series of dots on a two-dimensional graph, thereby creating a unique encoded image of the text. Other embodiments are disclosed.

What is claimed is: 
     
         1 . A device, comprising:
 a processing system including a processor; and   a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:
 transforming text comprising a series of characters into a series of dots arranged in a two-dimensional pattern, wherein each character in the series of characters is represented by four consecutive dots in the series of dots, and wherein each Cartesian coordinate of each dot in the series of dots is one unit away from a corresponding Cartesian coordinate of a same dimension as that of a preceding dot in the series of dots; and 
 plotting the series of dots on a two-dimensional graph based on the Cartesian coordinates of each dot, thereby creating a unique encoded image for the text. 
   
     
     
         2 . The device of  claim 1 , wherein the transforming further comprises: generating X and Y Cartesian coordinates for each dot in the series of dots from a first half and a second half of a numerical representation of a respective character of the series of characters. 
     
     
         3 . The device of  claim 2 , wherein the transforming comprises a lossless mapping of the text to the X and Y Cartesian coordinates. 
     
     
         4 . The device of  claim 3 , further comprising using the X and Y Cartesian coordinates for fast model training and prediction by natural language processing machine learning. 
     
     
         5 . The device of  claim 1 , wherein the two-dimensional graph is used for typography in augmented reality and/or virtual reality. 
     
     
         6 . The device of  claim 1 , wherein the operations further comprise coloring each dot in the series of dots based on an order of appearance in the series of dots. 
     
     
         7 . The device of  claim 1 , wherein the operations further comprise scanning the two-dimensional graph by machine learning to recognize the text. 
     
     
         8 . The device of  claim 7 , wherein the operations further comprise using the recognized text to effect navigation of a vehicle. 
     
     
         9 . The device of  claim 1 , wherein the two-dimensional graph is used for a street sign. 
     
     
         10 . The device of  claim 1 , wherein the processing system comprises a plurality of processors operating in a distributed computing environment. 
     
     
         11 . A non-transitory, machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising:
 transforming text comprising a series of characters into a series of dots, wherein each character in the series of characters is represented by four consecutive dots in the series of dots, and wherein each dot in the series of dots corresponds to X and Y Cartesian coordinates, wherein each Cartesian coordinate is one unit away from a corresponding Cartesian coordinate in a same dimension of a preceding dot in the series of dots; and   plotting the series of dots on a two-dimensional graph based on the X and Y Cartesian coordinates of each dot, thereby creating a unique encoded image for the text.   
     
     
         12 . The non-transitory, machine-readable medium of  claim 11 , wherein the two-dimensional graph comprises a street sign. 
     
     
         13 . The non-transitory, machine-readable medium of  claim 12 , wherein the street sign is recognized by machine learning to effect navigation of a vehicle. 
     
     
         14 . The non-transitory, machine-readable medium of  claim 12 , wherein the transforming further comprises: generating the X and Y Cartesian coordinates for each dot in the series of dots from a first half and a second half of a numerical representation of a respective character of the series of characters. 
     
     
         15 . The non-transitory, machine-readable medium of  claim 12 , wherein the transforming comprises a lossless mapping of the text to the X and Y Cartesian coordinates. 
     
     
         16 . The non-transitory, machine-readable medium of  claim 15 , further comprising using the X and Y Cartesian coordinates for fast model training and prediction by natural language processing machine learning. 
     
     
         17 . The non-transitory, machine-readable medium of  claim 12 , wherein the two-dimensional graph is used for typography in augmented reality and/or virtual reality. 
     
     
         18 . The non-transitory, machine-readable medium of  claim 12 , wherein the operations further comprise coloring each dot in the series of dots based on an order of appearance in the series of dots. 
     
     
         19 . A method, comprising:
 mapping, by a processing system comprising a processor, text comprising a series of characters into a series of dots, wherein each character in the series of characters is represented by four consecutive dots in the series of dots, and wherein each dot in the series of dots corresponds to X and Y Cartesian coordinates, wherein each Cartesian coordinate is one unit away from a corresponding Cartesian coordinate in a same dimension of a preceding dot in the series of dots; and   plotting, by the processing system, the series of dots on a two-dimensional graph based on the X and Y Cartesian coordinates of each dot, thereby creating a unique encoded image for the text   
     
     
         20 . The method of  claim 19 , further comprising scanning, by the processing system, the two-dimensional graph by machine learning to recognize the text, wherein the two-dimensional graph is a street sign, and wherein the text is used to effect navigation of a vehicle.