Optum Technology, Inc.

Nilav Baran Ghosh

V Kishore Ayyadevara

Vineet Shukla

Kartik Chaudhary

Santosh Kumar Jami

Snigdha Sree Borra

Yeshwanth Reddy

Yerraguntla Yeshwanth Reddy

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

Systems and methods are configured to extract text from images (e.g., document images) utilizing a combination of optical character recognition processes and neural network-based analysis of various images to train a machine-learning object recognition model that is configured to identify text within images based on object-comparisons between known and unknown text within images. In certain embodiments, identified text within images may be utilized to identify corresponding regions-of-interest for extraction of image data encompassing a portion of an image that may be indexed based at least in part on text identified as corresponding to the particular region-of-interest.

Automated systems and methods for identifying fields and regions of interest within a document image

Growing community of inventors

Hyderabad Telangana, India

Nilav Baran Ghosh