Systems and methods for processing images
Systems and methods for identifying landmarks of a document from a digital representation of the document. The method comprises accessing the digital representation of the document and operating a Machine Learning Algorithm (MLA), the MLA having been trained based on a set of training digital representations of documents associated with labels. The operating the MLA comprises down-sampling the digital representation of the document, detecting landmarks, generating fractional pixel coordinates for the detected landmarks. The method further determines the pixel coordinates of the landmarks by upscaling the fractional pixel coordinates from the second resolution to the first resolution and outputs the pixel coordinates of the landmarks.
1. A computer-implemented method of identifying landmarks of a document from a digital representation of the document, the method comprising:
accessing the digital representation of the document, the digital representation being associated with a first resolution;
operating a Machine Learning Algorithm (MLA), the MLA having been trained:
based on a set of training digital representations of documents associated with labels, the labels identifying landmarks of the documents represented by the training digital representations;
to learn a first function allowing detection of landmarks of documents represented by digital representations;
to learn a second function allowing generation of fractional pixel coordinates for the landmarks detected by the first function;
the operating the MLA comprising:
down-sampling the digital representation of the document, the down-sampled digital representation of the document being associated with a second resolution, the second resolution being lower than the first resolution;
detecting landmarks from the down sampled digital representation of the document;
generating fractional pixel coordinates for the detected landmarks in accordance with the second resolution, the fractional pixel coordinates allowing reconstructing pixel coordinates in accordance with the first resolution;
determining the pixel coordinates of the landmarks by upscaling the fractional pixel coordinates from the second resolution to the first resolution; and
outputting the pixel coordinates of the landmarks.
2. The method of claim 1 , wherein the MLA comprises a Convolutional Neural Network (CNN) comprising multiple layers, the multiple layers comprising a first layer implementing the learning of the first function and a second layer implementing the learning of the second function.
3. The method of claim 1 , wherein the labels identifying landmarks comprise coordinates.
4. The method of claim 1 , wherein the fractional pixel coordinates comprise floating values.
5. The method of claim 1 , wherein the first function implements a classification task, the classification task predicting whether a sub-portion of the digital representation of the document comprises a landmark.
6. The method of claim 5 , wherein the second function implements a regression task, the regression task generating fractional pixel coordinates from the sub-portion of the digital representation identified as comprising a landmark.
7. The method of claim 1 , wherein the landmarks comprise one of corners or edges.
8. A system for identifying landmarks of a document from a digital representation of the document, the system comprising:
at least one processor, and
memory storing a plurality of executable instructions which, when executed by the at least one processor, cause the system to:
access the digital representation of the document, the digital representation being associated with a first resolution;
operate a Machine Learning Algorithm (MLA), the MLA having been trained:
based on a set of training digital representations of documents associated with labels, the labels identifying landmarks of the documents represented by the training digital representations;
to learn a first function allowing detection of landmarks of documents represented by digital representations;
to learn a second function allowing generation of fractional pixel coordinates for the landmarks detected by the first function;
the operating the MLA comprising:
down-sampling the digital representation of the document, the down-sampled digital representation of the document being associated with a second resolution, the second resolution being lower than the first resolution;
detecting landmarks from the down sampled digital representation of the document;
generating fractional pixel coordinates for the detected landmarks in accordance with the second resolution, the fractional pixel coordinates allowing reconstructing pixel coordinates in accordance with the first resolution;
determining the pixel coordinates of the landmarks by upscaling the fractional pixel coordinates from the second resolution to the first resolution; and
outputting the pixel coordinates of the landmarks.
9. The system of claim 8 , wherein the MLA comprises a Convolutional Neural Network (CNN) comprising multiple layers, the multiple layers comprising a first layer implementing the learning of the first function and a second layer implementing the learning of the second function.
10. The system of claim 8 , wherein the labels identifying landmarks comprise coordinates.
11. The system of claim 8 , wherein the fractional pixel coordinates comprise floating values.
12. The system of claim 8 , wherein the first function implements a classification task, the classification task predicting whether a sub-portion of the digital representation of the document comprises a landmark.
13. The system of claim 12 , wherein the second function implements a regression task, the regression task generating fractional pixel coordinates from the sub-portion of the digital representation identified as comprising a landmark.
14. The system of claim 8 , wherein the landmarks comprise one of corners or edges.