Adobe, Inc.

Other

Ankit Bal

Prasenjit Mondal

Anuj Shara

Walter Wei-Tuh Chang

Kenneth Edward Feuerman

Shantanu Kumar

Ram Bhushan Agrawal

Mohit Gupta

Uttam Dwivedi

Jayant Vaibhav Srivastava

Siddharth Kumar

Deepanshu Arora

Tarun Verma

Punit Singh

Techniques for document decomposition based on determined logical visual layering of document content. The techniques include iteratively identifying a plurality of logical visual layers of a document resulting in each logical visual layer being associated with one or more document content objects of the document. The one or more document content objects associated with each logical visual layer are annotated to be indicative of the associated logical visual layer. The document is then displayed with an indication of one or more of the annotated document objects.

Document decomposition based on determined logical visual layering of document content

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately enhancing optical character recognition with a machine learning approach for determining words from reverse text, vertical text, and atypically-sized text. For example, the disclosed systems segment a digital image into text regions and non-text regions utilizing an object detection machine learning model. Within the text regions, the disclosed systems can determine reverse text glyphs, vertical text glyphs, and/or atypically-sized text glyphs utilizing an edge based adaptive binarization model. Additionally, the disclosed systems can utilize respective modification techniques to manipulate reverse text glyphs, vertical text glyphs, and/or atypically-sized glyphs for analysis by an optical character recognition model. The disclosed systems can further utilize an optical character recognition model to determine words from the modified versions of the reverse text glyphs, the vertical text glyphs, and/or the atypically-sized text glyphs.

Utilizing machine-learning based object detection to improve optical character recognition

Various methods and systems for performing analytics based on hierarchical categorization of content are provided. Analytics can be performed using an index building workflow and a classification workflow. In the index building workflow, documents are received and analyzed to extract features from the documents. Hierarchical category paths can be identified for the features. The documents are indexed to support searching the documents for the hierarchical category paths. In the classification workflow, a query, that includes or references content, may be received and analyzed to extract features from the content. The features are executed against a search engine that returns search result documents associated with hierarchical category paths. The hierarchical category paths from the search result documents may be used to generate a topic model of the content associated with the query. The topic model, used for web analytics, includes scores for the hierarchical category paths and for enumerated category topics.

Analytics based on scalable hierarchical categorization of web content

While a user holds a camera positioned relative to an object, a first image of the object and a second image of the object, as captured by the camera, may be obtained. Intensity variations between a first intensity map of the first image and a combination intensity map obtained from the first intensity map and a second intensity map of the second image may be compared. Then, a shadow may be identified within the first image, based on the intensity variations.

Image shadow detection using multiple images

Techniques are disclosed for document boundary detection (BD) from an input image using a combination of deep learning model and image processing algorithms. Quadrilaterals approximating the document boundaries in the input image are determined and rated separately using both these approaches: deep leaning using convolutional neural network (CNN) and heuristics using image processing algorithms. Thereafter, the best rated quadrilateral is selected from the quadrilaterals obtained from both the approaches.

Document boundary detection using deep learning model and image processing algorithms

There are described mobile devices, and methods thereof, for real time analyses of captured images. The device determines whether a light level of a first image meets or exceeds a low light threshold. The device also determines whether a shadow level of the first image meets or exceeds a shadow threshold in response to determining the light level does not meet or exceed the low light threshold. The light source is activated in response to determining the light level meets or exceeds the low light threshold or the shadow level meets or exceeds the shadow threshold. The device further determines whether a glare level of a second image meets or exceeds a glare threshold in response to identifying the second image. If so, then a final image is captured with the light source inactive. Otherwise, the final image is captured with the light source active.

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Noida, India

Ankit Bal