Optum Technology, Inc.

Yerraguntla Yeshwanth Reddy

V Kishore Ayyadevara

Kartik Chaudhary

Raghav Bali

Santosh Kumar Jami

Snigdha Sree Borra

Nilav Baran Ghosh

There is a need for more effective and efficient predictive document conversion. This need can be addressed by, for example, solutions for performing document conversion using a trained convolutional neural document conversion machine learning. In one example, the trained convolutional neural document conversion machine learning model is associated with a preprocessing block having a plurality of preprocessing subblocks, one or more main processing blocks each having a plurality of main processing subblocks, and a plurality of postprocessing subblocks each having one or more postprocessing subblocks, and the trained convolutional neural document conversion machine learning model is further associated with a preprocessing subblock repetition count hyper-parameter that defines a preprocessing subblock count of the plurality of preprocessing subblocks.

Predictive document conversion

Solutions for more efficient and effective optical character recognition with respect to an input text segment are disclosed. In one example, a method includes processing an input text image using a deep character overlap detection machine learning model in order to generate a character map for the input text image, an overlap map for the input text image, and an affinity map for the input text image; generating an overlap-aware word boundary recognition output based at least in part on the character map, the overlap map, and the affinity map, wherein the overlap-aware word boundary recognition output describes one or more inferred word regions of the input text image; and performing one or more prediction-based actions based at least in part on the overlap-aware word boundary recognition output.

Overlap-aware optical character recognition

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Hyderabad Telangana, India

Yerraguntla Yeshwanth Reddy