Tata Consultancy Services Limited

Optum Technology, Inc.

Santosh Kumar Jami

Srinivasa Rao Chalamala

Krishna Rao Kakkirala

Arun Kumar Jindal

Ajeet Kumar Singh

Bala Mallikarjunarao Garlapati

Balakrishna Gudla

V Kishore Ayyadevara

Snigdha Sree Borra

Sachin Premsukh Lodha

Vijayanand Mahadeo Banahatti

Vineet Shukla

Nilav Baran Ghosh

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

Biometric templates (template) are used to store biometric data of one or more users. As biometric data of each user is unique and non-replaceable, template security is a major concern. This disclosure relates generally to template protection, and more particularly to a method and system for biometric template protection. The system generates a final perturbation value for data in the template, based on a first binary code (BC), a second binary code (BC'), feature vectors corresponding to data in the template, and an initial random perturbation. The final perturbation and the feature vectors are mapped to the first binary code. The first binary code represents the template. Every time the first binary code is compromised, the value of first binary code is updated, and the whole process is repeated to generate corresponding final perturbation and then the mapping is done.

Method and system for biometric template protection

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

Systems and methods for insertion of a watermark into images and tampering detection of the watermarked images by a Convolutional Neural Network (CNN) technique. The traditional systems and methods provide for detecting the tampering of the watermarked images by simply identifying a presence of an inserted watermark into an image but none them provide for inserting a random sequence into input image(s) and then detect the tampering by classifying the input image(s) by a neural network. Embodiments of the present disclosure provide for insertion of the watermark into the input image(s) and tampering detection of the watermarked images by training a Convolutional Neural Network (CNN)to classify the images as tampered or non-tampered, extracting random noise, obtaining non-classified watermarked images from the random noise, and obtaining, from the non-classified watermarked images, classified watermarked images and detecting an absence or a presence of the tampering based upon the classified watermarked images.

Neural network based insertion of watermark into images and tampering detection thereof

System and method for text localization in images are disclosed. In an embodiment, a line and graphic eliminated image is received. Further, horizontal projection is performed on rows of the image to obtain a first flag vector, the flag vector indicates whether there is text in each row. Furthermore, a number of run-lengths of consecutive 1's and 0's is computed in the first flag vector. Moreover, text lines is extracted in the image based on the computed number of run-lengths of consecutive 1's and 0's in the first flag vector. Also, vertical projection is performed on the text lines to obtain a second flag vector for the text lines. Further, a number of run-lengths of consecutive 1's and 0's is computed in the second flag vectors. Furthermore, text is localized in the image based on the computed number of run-lengths of consecutive 1's and 0's in the second flag vectors.

System and method for text localization in images

Growing community of inventors

Hyderabad Telangana, India

Santosh Kumar Jami