Abstract: A method and system for performing on-device image to text conversion are provided. Embodiments herein relates to the field of performing image to text conversion and more particularly to performing on-device image to text conversion with an improved accuracy. A method performing on-device image to text conversion is provided. The method includes language detection from an image, understanding of text in an edited image and using a contextual and localized lexicon set for post optical character recognition (OCR) correction.

Abstract: Accordingly, embodiments herein disclose a method and apparatus for retrieving intelligent information from an electronic device (100). The method includes receiving, by the electronic device (100), an input from a user. Further, the method includes identifying, by the electronic device (100), at least one data item to generate at least one metadata tag. Further, the method includes automatically generating, by the electronic device (100), the at least one metadata tag related to the at least one data item based on a plurality of parameters. Further, the method includes providing, by the electronic device (100), at least one priority to the at least one metadata tag. Further, the method includes storing, by the electronic device (100), the at least one metadata tag at the electronic device (100).

Abstract: A method and system for performing on-device image to text conversion are provided. Embodiments herein relates to the field of performing image to text conversion and more particularly to performing on-device image to text conversion with an improved accuracy. A method performing on-device image to text conversion is provided. The method includes language detection from an image, understanding of text in an edited image and using a contextual and localized lexicon set for post optical character recognition (OCR) correction.

Abstract: Embodiments herein provide a method for managing private content by an electronic device (100). The method includes: displaying content on a screen of the electronic device (100), wherein the content is stored in a non-secure folder of the electronic device (100) and extracting a feature(s) of the displayed content to categorize the displayed content using a machine learning (ML) model. Further, the method includes categorizing the displayed content into the private content or public content based on globally objectionable private content and user-specific objectionable private content. Further, the method includes displaying a recommendation on the screen of the electronic device (100) for transferring the displayed content into a secure folder of the electronic device (100), detecting an event to transfer the private content into the secure folder of the electronic device (100), and automatically transferring the private content from the non-secure folder to the secure folder of the electronic device (100).

Abstract: Accordingly, embodiments herein disclose a method and apparatus for retrieving intelligent information from an electronic device (100). The method includes receiving, by the electronic device (100), an input from a user. Further, the method includes identifying, by the electronic device (100), at least one data item to generate at least one metadata tag. Further, the method includes automatically generating, by the electronic device (100), the at least one metadata tag related to the at least one data item based on a plurality of parameters. Further, the method includes providing, by the electronic device (100), at least one priority to the at least one metadata tag. Further, the method includes storing, by the electronic device (100), the at least one metadata tag at the electronic device (100).

Abstract: A method for intelligent screen capture in an electronic device includes: receiving, by the electronic device, a user input for capturing a screenshot of contents displayed on a screen of the electronic device; dividing, by the electronic device, the screen of the electronic device into a plurality of blocks, each of the plurality of blocks including inter-related contents displayed on the screen of the electronic device; identifying, by the electronic device, at least one block from the plurality of blocks based on a plurality of parameters; and displaying, by the electronic device, a guide user interface for capturing the screenshot of the at least one block.

Abstract: A method for intelligent screen capture in an electronic device includes: receiving, by the electronic device, a user input for capturing a screenshot of contents displayed on a screen of the electronic device; dividing, by the electronic device, the screen of the electronic device into a plurality of blocks, each of the plurality of blocks including inter-related contents displayed on the screen of the electronic device; identifying, by the electronic device, at least one block from the plurality of blocks based on a plurality of parameters; and displaying, by the electronic device, a guide user interface for capturing the screenshot of the at least one block.

Abstract: First information is captured based on monitoring interactions between a user of a communication device and multiple entities. Second information is determined based on monitoring a schedule including upcoming scheduled activities. An incoming call indication and at least a calling phone number related to an incoming call for the communication device are received. Contextual information related to at least the calling phone number and a relevance factor indicating a level of relevance of the incoming call are determined based on the first information and the second information. An indication of the incoming call, the contextual information, and the relevance factor are provided on the communication device.

Abstract: Automatic determination of caller identification information and encoding of such information in outgoing data requests, such a API calls to a call management component, are disclosed herein. The caller identification information may be determined by analyzing information associated with a call target to determine if the target has a custom outbound telephone number assigned, was contacted through a specific campaign, has local geographic data, is being called from a specific entity, or is being made from a call originator with a specific outbound number set. The system may execute machine learning on previous calling data in order to update caller identification selection models used for other outgoing calls.

Abstract: Automatic determination of caller identification information and encoding of such information in outgoing data requests, such a API calls to a call management component, are disclosed herein. The caller identification information may be determined by analyzing information associated with a call target to determine if the target has a custom outbound telephone number assigned, was contacted through a specific campaign, has local geographic data, is being called from a specific entity, or is being made from a call originator with a specific outbound number set. The system may execute machine learning on previous calling data in order to update caller identification selection models used for other outgoing calls.

Abstract: Automatic determination of caller identification information and encoding of such information in outgoing data requests, such a API calls to a call management component, are disclosed herein. The caller identification information may be determined by analyzing information associated with a call target to determine if the target has a custom outbound telephone number assigned, was contacted through a specific campaign, has local geographic data, is being called from a specific entity, or is being made from a call originator with a specific outbound number set. The system may execute machine learning on previous calling data in order to update caller identification selection models used for other outgoing calls.