Abstract: Disclosed are a system, method and apparatus for classification of data in a machine learning system. In one aspect, a method for classification of data in a machine learning system through one or more computer processors is disclosed. Further, generating, through one or more computer processors, a data classifier using a first dataset and determining an accuracy value of the data classifier to achieve a predefined model accuracy threshold. Still further, iterating, through one or more computer processors, calibration of the first dataset based on a set of parameters until the accuracy value matches or exceeds the predefined model accuracy threshold value. Further, the calibration comprises a user input to indicate a correctness of a presented subset of data from a second dataset and using the above to generate an enhanced data classifier for the classification of data.

Abstract: The present disclosure discloses a method and an object determination system for determining one or more target objects in an image. The image is segmented by the object detection system into one or more segments based on visual attributes in a first set. Morphological operations are performed on the one or more segments to obtain one or more morphed segments. One or more candidates of target objects are identified based on visual attributes in a second set corresponding to each one or more morphed segments. The object determination system identifies at least one of true positive and false positive from the one or more candidates which indicates presence or absence of the one or more target objects respectively, based on neighborhood information associated with the one or more candidates. The present disclosure facilitates in determining target objects in document automatically, thereby eliminating manual intervention in identifying target objects in the document.

Abstract: Disclosed are a system, method and apparatus for classification of data in a machine learning system. In one aspect, a method for classification of data in a machine learning system through one or more computer processors is disclosed. Further, generating, through one or more computer processors, a data classifier using a first dataset and determining an accuracy value of the data classifier to achieve a predefined model accuracy threshold. Still further, iterating, through one or more computer processors, calibration of the first dataset based on a set of parameters until the accuracy value matches or exceeds the predefined model accuracy threshold value. Further, the calibration comprises a user input to indicate a correctness of a presented subset of data from a second dataset and using the above to generate an enhanced data classifier for the classification of data.

Abstract: The present disclosure discloses a method and an object determination system for determining one or more target objects in an image. The image is segmented by the object detection system into one or more segments based on visual attributes in a first set. Morphological operations are performed on the one or more segments to obtain one or more morphed segments. One or more candidates of target objects are identified based on visual attributes in a second set corresponding to each one or more morphed segments. The object determination system identifies at least one of true positive and false positive from the one or more candidates which indicates presence or absence of the one or more target objects respectively, based on neighborhood information associated with the one or more candidates. The present disclosure facilitates in determining target objects in document automatically, thereby eliminating manual intervention in identifying target objects in the document.