System for character recognition in a digital image processing environment
地域：
NC
NC Charlotte
摘要
Systems, computer program products, and methods are described herein for character recognition in a digital image processing environment. The present invention is configured to electronically retrieve one or more documents from a document repository, wherein the one or more documents are in an image format; initiate one or more image super resolution algorithms on the one or more documents; generate, based on at least the one or more image super resolution algorithms, one or more high-resolution images associated with each of the one or more documents; initiate one or more image bottleneck ensembles (IBE) algorithms on the one or more high-resolution images; extract, using the one or more IBE algorithms, one or more features associated with the one or more high resolution images; and store the one or more features extracted from the one or more high resolution images in a feature repository.
說(shuō)明書(shū)
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The present invention embraces a system for character recognition in a digital image processing environment.
In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. OCR (optical character recognition) is the recognition of printed or written text characters by a computer. This involves photo-scanning of the text character-by-character, analysis of the scanned-in image, and then translation of the character image into character codes, such as ASCII, commonly used in data processing. CNNs can be leveraged to recognize text characters without the need to photo-scan the text character-by-character.
There is a need for a system for character recognition in a digital image processing environment by leveraging the advantages provided by CNNs.
The following presents a simplified summary of one or more embodiments of the present invention, in order to provide a basic understanding of such embodiments. This summary is not an extensive overview of all contemplated embodiments, and is intended to neither identify key or critical elements of all embodiments nor delineate the scope of any or all embodiments. Its sole purpose is to present some concepts of one or more embodiments of the present invention in a simplified form as a prelude to the more detailed description that is presented later.
權(quán)利要求
What is claimed is:1. A system for character recognition in a digital image processing environment, the system comprising:at least one non-transitory storage device; and at least one processing device coupled to the at least one non-transitory storage device, wherein the at least one processing device is configured to: electronically retrieve one or more documents from a document repository, wherein the one or more documents are in an image format; initiate one or more image super resolution algorithms on the one or more documents; generate, based on at least the one or more image super resolution algorithms, one or more high-resolution images associated with each of the one or more documents; initiate one or more image bottleneck ensembles (BE) algorithms on the one or more high-resolution images; extract, using the one or more IBE algorithms, one or more features associated with the one or more high resolution images; store the one or more features extracted from the one or more high resolution images in a feature repository, electronically receive a query from a computing device of the user, wherein the query is in a text format, wherein the query is associated with an original font; initiate a data normalization algorithm on the query; normalize, using the data normalization algorithm, the query, wherein normalizing further comprises transforming the original font of the query to a first font; and generate an image of the query, wherein the query is associated with the first font. 2. The system of claim 1 , wherein the at least one processing device is further configured to:electronically receive the query from the computing device associated with the user, wherein the query is in a text format; generate the image of the query, wherein generating further comprises converting the query from the text format to an image format; initiate one or more feature extraction algorithms on the image of the query; and extract, using the one or more feature extraction algorithms, one or more features associated with the image of the query. 3. The system of claim 2 , wherein the one or more features associated with the image of the query comprises one or more sets of ordered points that define one or more contours of one or more portions of the image of the query. 4. The system of claim 2 , wherein the at least one processing device is further configured to:determine that the one or more documents retrieved from the document repository are associated with one or more unique fonts; initiate a data normalization algorithm on the one or more documents; normalize, using the data normalization algorithm, the one or more documents, wherein normalizing further comprises at least transforming the one or more fonts to the first font; and initiate the one or more image super resolution algorithms on the one or more documents, wherein the one or more documents are associated with the first font. 5. The system of claim 2 , wherein the at least one processing device is further configured to:electronically retrieve the one or more features associated with the one or more high resolution images from the feature repository; initiate a sliding window matching algorithm on the one or more features associated with the one or more high resolution images and the one or more features associated with the image of the query; compare, using the sliding window matching algorithm, the one or more features associated with the image of the query with the one or more features associated with the one or more high resolution images; and determine one or more matches between the one or more features associated with the one or more images of the query and the one or more features associated with the one or more high resolution images. 6. The system of claim 5 , wherein the at least one processing device is further configured to initiate the sliding window matching algorithm, wherein initiating further comprises:establishing a window with a scale of a predetermined width; sliding, sequentially, the window across a surface of the one or more high resolution images at one or more predetermined incremental steps; identifying at least a portion of the one or more features associated with the one or more high resolution images within the window at each of the one or more predetermined incremental steps; comparing the one or more features associated with the image of the query with at least the portion of the one or more features associated with the one or more high resolution images identified within the window at each of the one or more predetermined incremental steps; and determine the one or more matches between the one or more images of the query and at least the portion of the one or more features associated with the one or more high resolution images identified within the scale at each of the one or more predetermined incremental steps. 7. The system of claim 6 , wherein the predetermined width associated with the window is based on at least a length of the query, wherein the query is associated with the first font. 8. The system of claim 1 , wherein the at least one processing device is further configured to extract the one or more features associated with the one or more high resolution images, wherein extracting further comprises:initiating the one or more IBE algorithms on the one or more high resolution images, wherein the one or more IBE algorithms comprises at least one or more convolutional neural networks; processing, in parallel, the one or more high resolution images using the one or more IBE algorithms; and extracting one or more preliminary features associated with the one or more high resolution images from each of the one or more IBE algorithms based on at least processing the one or more high resolution images using the one or more IBE algorithms; and concatenating the one or more preliminary features to generate the one or more features associated with the one or more high resolution images. 9. A computer program product for character recognition in a digital image processing environment, the computer program product comprising a non-transitory computer-readable medium comprising code causing a first apparatus to:electronically retrieve one or more documents from a document repository, wherein the one or more documents are in an image format; initiate one or more image super resolution algorithms on the one or more documents; generate, based on at least the one or more image super resolution algorithms, one or more high-resolution images associated with each of the one or more documents; initiate one or more image bottleneck ensembles (IBE) algorithms on the one or more high-resolution images; extract, using the one or more IBE algorithms, one or more features associated with the one or more high resolution images; store the one or more features extracted from the one or more high resolution images in a feature repository; electronically receive a query from a computing device of the user, wherein the query is in a text format, wherein the query is associated with an original font; initiate a data normalization algorithm on the query; normalize, using the data normalization algorithm, the query, wherein normalizing further comprises transforming the original font of the query to a first font; and generate an image of the query, wherein the query is associated with the first font. 10. The computer program product of claim 9 , wherein the first apparatus is further configured to:electronically receive the query from the computing device associated with the user, wherein the query is in a text format; generate the image of the query, wherein generating further comprises converting the query from the text format to an image format; initiate one or more feature extraction algorithms on the image of the query; and extract, using the one or more feature extraction algorithms, one or more features associated with the image of the query. 11. The computer program product of claim 10 , wherein the one or more features associated with the image of the query comprises one or more sets of ordered points that define one or more contours of one or more portions of the image of the query. 12. The computer program product of claim 10 , wherein the first apparatus is further configured to:determine that the one or more documents retrieved from the document repository are associated with one or more unique fonts; initiate a data normalization algorithm on the one or more documents; normalize, using the data normalization algorithm, the one or more documents, wherein normalizing further comprises at least transforming the one or more fonts to the first font; and initiate the one or more image super resolution algorithms on the one or more documents, wherein the one or more documents are associated with the first font. 13. The computer program product of claim 10 , wherein the first apparatus is further configured to:electronically retrieve the one or more features associated with the one or more high resolution images from the feature repository; initiate a sliding window matching algorithm on the one or more features associated with the one or more high resolution images and the one or more features associated with the image of the query; compare, using the sliding window matching algorithm, the one or more features associated with the image of the query with the one or more features associated with the one or more high resolution images; and determine one or more matches between the one or more features associated with the one or more images of the query and the one or more features associated with the one or more high resolution images. 14. The computer program product of claim 13 , wherein the first apparatus is further configured to initiate the sliding window matching algorithm, wherein initiating further comprises:establishing a window with a scale of a predetermined width; sliding, sequentially, the window across a surface of the one or more high resolution images at one or more predetermined incremental steps; identifying at least a portion of the one or more features associated with the one or more high resolution images within the window at each of the one or more predetermined incremental steps; comparing the one or more features associated with the image of the query with at least the portion of the one or more features associated with the one or more high resolution images identified within the window at each of the one or more predetermined incremental steps; and determine the one or more matches between the one or more images of the query and at least the portion of the one or more features associated with the one or more high resolution images identified within the scale at each of the one or more predetermined incremental steps. 15. The computer program product of claim 14 , wherein the predetermined width associated with the window is based on at least a length of the query, wherein the query is associated with the first font. 16. The computer program product of claim 9 , wherein the first apparatus is further configured to extract the one or more features associated with the one or more high resolution images, wherein extracting further comprises:initiating the one or more IBE algorithms on the one or more high resolution images, wherein the one or more IBE algorithms comprises at least one or more convolutional neural networks; processing, in parallel, the one or more high resolution images using the one or more IBE algorithms; and extracting one or more preliminary features associated with the one or more high resolution images from each of the one or more IBE algorithms based on at least processing the one or more high resolution images using the one or more IBE algorithms; and concatenating the one or more preliminary features to generate the one or more features associated with the one or more high resolution images. 17. A method for character recognition in a digital image processing environment, the method comprising:electronically retrieving one or more documents from a document repository, wherein the one or more documents are in an image format; initiating one or more image super resolution algorithms on the one or more documents; generating, based on at least the one or more image super resolution algorithms, one or more high-resolution images associated with each of the one or more documents; initiating one or more image bottleneck ensembles (IBE) algorithms on the one or more high-resolution images; extracting, using the one or more IBE algorithms, one or more features associated with the one or more high resolution images; storing the one or more features extracted from the one or more high resolution images in a feature repository, electronically receiving a query from a computing device of the user, wherein the query is in a text format, wherein the query is associated with an original font; initiating a data normalization algorithm on the query; normalizing, using the data normalization algorithm, the query, wherein normalizing further comprises transforming the original font of the query to a first font; and generating an image of the query, wherein the query is associated with the first font. 18. The method of claim 17 , wherein the method further comprises:electronically receiving the query from the computing device associated with the user, wherein the query is in a text format; generating the image of the query, wherein generating further comprises converting the query from the text format to an image format; initiating one or more feature extraction algorithms on the image of the query; and extracting, using the one or more feature extraction algorithms, one or more features associated with the image of the query.
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