Tata Consultancy Services Limited

Dinesh Yadav

Amit Kumar Agrawal

Devang Jagdishchandra Patel

Prabhat Ranjan Mishra

Chirabrata Bhaumik

Prosenjit Mondal

Pushp Kumar Jain

Rajdeep Chatterjee

Ketkee Pandit

Mantu Prasad Gupta

Ankita Gupta

Tanmay Suhas Jagtap

ROI (Region of Interest) detection is an important step in extracting relevant information from a document image. Such images are very high-resolution images in nature and size of images is in order of megabytes, which makes text detection pipeline very slow. Traditional methods detect and extract ROI from images, but these work only for specific image types. Other approaches include deep learning (DL) based methods for ROI detect which need intensive training and require high end computing infrastructure/resources with graphical processing unit (GPU) capabilities. Systems and methods of the present disclosure perform ROI extraction by partitioning input image into parts based on its visual perception and then classify the image in first or second category. Region of interest is extracted from a resized image based on the classification by applying image processing techniques. Further, the system determines whether the input image is a pre-cropped image or a normal scanned image.

Extracting region of interest from scanned images and determining an associated image type thereof

This disclosure relates generally to method and system for determining label value for labels in unstructured documents. Typical systems have challenge in understanding variations in layout of unstructured documents and extract information therefrom. The disclosed method and system facilitate systematically identifying sections and bounding boxes in the page images, taking image portion of the bounding boxes and extracting labels and label values therefrom. In case the label values are not present in the same bounding box having the label, the neighboring labels are examined for the matching label values. The system also obtains label-label value pairs from the document by utilizing a trained deep learning model, and compares the output with the label-label value pairs extracted earlier. An aggregated confidence score is assigned to the text in the bounding box.

System and method for determination of label values in unstructured documents

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Mumbai, India

Dinesh Yadav