An image processing method, an image processing device, an electronic device, and a storage medium are provided. The image processing method includes: obtaining an input image, wherein the input image includes M character rows; performing global correction processing on the input image to obtain an intermediate corrected image; determining the M character row lower boundaries; determining the relative offset of all pixels in the intermediate corrected image according to the M character row lower boundaries, the first image boundary and the second image boundary of the intermediate corrected image; determining the local adjustment offset of all pixels in the intermediate corrected image according to the relative offsets of all pixels in the intermediate corrected image; and performing local adjustment on the intermediate corrected image according to the local adjustment offsets of all pixels in the intermediate corrected image to obtain the target corrected image.

An image processing device includes multiple image processing units, each trained to accommodate a different feature possibly contained in an image, a decision unit that decides a sequence of the multiple image processing units according to the features contained in an input image, and an application unit that applies the image processing units to the input image in the sequence decided by the decision unit.

The present disclosure provides systems, methods, and computer program products for generating a digital representation of a system from engineering documents of the system comprising one or more schematics and a components table. An example method can comprise (a) classifying, using a deep learning algorithm, (i) each of a plurality of symbols in the one or more schematics as a component and (ii) each group of related symbols as an assembly, (b) determining connections between the components and the assemblies, (c) associating a subset of the components and the assemblies with entries in the components table; and (d) generating the digital representation of the system from the components, the assemblies, the connections, and the associations. The digital representation of the system can comprise at least a digital model of the system and a machine-readable bill of materials.

A computer-implemented method for increasing a recognition rate of an optical character recognition (OCR) system is provided. The method includes preprocessing by receiving an image, and extracting all vertical lines from the image. The method includes adding vertical lines at character areas of the image, extracting all horizontal lines from the image, and creating an unlined image removing all the vertical/horizontal lines from the image. The method further includes determining a border of a vertical direction of the unlined image based on the total of pixels of rows in each column, and adding vertical/horizontal auxiliary lines between characters of the unlined image. The method also includes postprocessing by receiving garbled words of OCR output, removing noise after morphologically analyzing, replacing garbled letters with correct ones based on a frequent edit operation, and outputting the correct word, weighting results of image distance calculations based on machine learning.

A method to reduce background noise in a document image. The method includes extracting, from the document image, a connected component corresponding to a background of the document image, generating a histogram of pixel values of the connected component, generating, using a non-linear mapping function based on the histogram, a non-linear probability distribution of the pixel values in the connected component, generating, based at least on a comparison between the non-linear probability distribution and a predetermined threshold, a replacement range of the pixel values, selecting, from the connected component, a pixel having a pixel value within the replacement range, and converting the pixel value of the pixel to a uniform background color.

A method and system are provided for removing noise from document images using a neural network-based machine learning model. A dataset of original document images is used as an input source of images. Random noise is added to the original document images to generate noisy images, which are provided to a neural network-based denoising system that generates denoised images. Denoised images and original document images are evaluated by a neural network-based discriminator system, which generates a predictive output relating to authenticity of evaluated denoised images. Feedback is provided backpropagation updates to train both the denoising and discriminator systems. Training sequences are iteratively performed to provide the backpropagation updates, such that the denoising system is trained to generate denoised images that can pass as original document images while the discriminator system is trained to improve the accuracy in predicting the authenticity of the images presented.

The subject matter of this specification can be implemented in, among other things, a method including identifying one or more blocks in an electronic image that depicts text characters. The method includes identifying one or more text blocks among the blocks that depict the text characters. The method includes identifying a text contrast value for each of the text blocks. The method includes identifying a type for each pixel in each of the text blocks based on the text contrast value. The method includes determining, for each pixel in each of the text blocks, a brightness for the pixel based on the identified type. The method includes storing, in at least one memory, the electronic image including the determined brightness for each pixel in each of the text blocks.

A system and method for automating and improving tabular and list-based data extraction from a variety of document types is disclosed. The system and method detect and sort which documents include tables or lists, and performs row and column segmentation. In addition, the system and method apply Conditional Random Fields models to localize each table and semantic data understanding to map and export the extracted data to the desired format and arrangement.

A system and method for automating and improving tabular and list-based data extraction from a variety of document types is disclosed. The system and method detect and sort which documents include tables or lists, and performs row and column segmentation. In addition, the system and method apply Conditional Random Fields models to localize each table and semantic data understanding to map and export the extracted data to the desired format and arrangement.

A system and method. The system may include a display, a lens having distortion, an image generator, and a processor. The lens may be configured to focus light received from an environment. The image generator may be configured to receive the light from the lens and output a stream of images as image data, wherein each of the stream of images is distorted. The processor may be configured to: receive the image data from the image generator; detect a point source object in the stream of images of the image data; enhance the point source object in the stream of images of the image data; undistort the stream of images of the image data having an enhanced point source object; and output a stream of undistorted images as undistorted image data to the display.