This disclosure relates to method and system for detecting orientation. The method includes detecting a plurality of regions in a document image, each region including text data, and determining positional information of each of the regions; for each of the plurality of regions, determining a region orientation to be one of first orientation or second orientation based on height and width of the region; determining a ratio of number of regions having first orientation and number of regions having second orientation; determining page orientation of the image as third orientation or second orientation, or rotating the image by 90 in counter-clockwise direction based on the ratio; determining first optical character recognition (OCR) data and second OCR data corresponding to the image and the image rotated by 180, respectively; and determining number of correct words in first OCR data and second OCR data based on comparison with dictionary data.

A method implements masked text processing for information processing with documents. The method involves receiving a document page as an image including text image data. The method further involves extracting text unit data and text location data from the image corresponding to the text image data using an optical character recognition (OCR) engine. The method further involves generating mask data for the text unit data with color data based on text type data. The method further involves producing a masked image by replacing the text image data with the mask data using the color data with the location data in the image. The method further involves transmitting the masked image to a machine learning model to execute a downstream task.

Systems and methods for tracking inventory items in an area of real space are disclosed. The methods can include receiving a signal generated in dependence on sensors, and detecting an item in the area of real space based on the received signal. One method includes implementing a trained location detection model to determine, based on inputs, whether an inventory item identified in the portion of the image has changed a position in the area of real space. Another method includes implementing a trained size determination model to determine, based on inputs, the size of an inventory item detected in the area of real space.