Document segmentation for optical character recognition
View Patent ↗An approach to identifying text within an image may be presented. The approach can receive an image. The approach can classify an image on a pixel-by-pixel basis whether the pixel is text. The approach can generate bounding boxes around groups of pixels that are classified as text. The approach can mask sections of an image that where pixels are not classified as text. The approach may be used as a pre-processing technique for optical character recognition in documents, scanned images, or still images.
1. A computer-implemented method for detecting text within an image, the computer-implemented method comprising:
training, by a processor, a model to detect text within the image, with a plurality of synthesized noisy images containing text, wherein the model comprises a Unet convolutional neural network architecture;
receiving, by the processor, an input image;
generating, by the processor, a pixel probability estimate for each pixel in the input image, based at least in part on the Unet convolutional neural network architecture, wherein the probability estimate is the probability a pixel is part of a segmented text;
generating, by the processor, a segmentation map based at least in part on the per pixel probability estimate for each pixel in the input image;
generating, by the processor, one or more bounding boxes around the detected text, based at least in part on the segmentation map; and
masking, by the processor, one or more sections of the input image outside of the bounding boxes.
2. The computer-implemented method of claim 1 , wherein the model is based on a neural network architecture.
3. The computer-implemented method of claim 2 , wherein the neural network architecture is a convolutional neural network architecture.
4. The computer-implemented method of claim 1 , further comprising:
sending, by the processor, the masked image to an optical character recognition (OCR) engine.
5. The computer-implemented method of claim 4 , wherein the OCR engine is based on a tesseract OCR engine.
6. A computer program product for detecting text within an image, the computer program product comprising:
one or more non-transitory computer readable storage media and program instructions stored on the one or more non-transitory computer readable storage media, the program instructions comprising program instructions to:
train a model to detect text within the image, with a plurality of synthesized noisy images containing text, wherein the model comprises a Unet convolutional neural network architecture;
receive an input image;
generate a pixel probability estimate for each pixel in the input image, based at least in part on the Unet convolutional neural network architecture, wherein the probability estimate is the probability a pixel is part of a segmented text;
generate a segmentation map based at least in part on the per pixel probability estimate for each pixel in the input image;
generate one or more bounding boxes around the detected text, based at least in part on the segmentation map; and
mask one or more sections of the input image outside of the bounding boxes.
7. The computer program product of claim 6 , wherein the model is based on a neural network architecture.
8. The computer program product of claim 7 , wherein the neural network architecture is a convolutional neural network architecture.
9. The computer program product of claim 6 , further comprising instructions to:
send the masked image to an optical character recognition (OCR) engine.
10. The computer program product of claim 9 , wherein the OCR engine is based on a tesseract OCR engine.
11. A computer system for detecting text within an image, the computer system comprising:
one or more computer processors;
one or more computer readable storage media; and
program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more processors, the program instructions comprising:
train a model to detect text within the image, with a plurality of synthesized noisy images containing text, wherein the model comprises a Unet convolutional neural network architecture;
receive an input image;
generate a pixel probability estimate for each pixel in the input image, based at least in part on the Unet convolutional neural network architecture, wherein the probability estimate is the probability a pixel is part of a segmented text generate a segmentation map based at least in part on the per pixel probability estimate for each pixel in the input image;
generate one or more bounding boxes around the detected text, based at least in part on the segmentation map; and
mask one or more sections of the input image outside of the bounding boxes.
12. The computer system of claim 11 , wherein the model is based on a neural network architecture.
13. The computer system of claim 11 , further comprising instructions to:
send the masked image to an optical character recognition (OCR) engine.
14. The computer system of claim 13 , wherein the OCR engine is based on a tesseract OCR engine.