Computer vision applications for document image understanding (DIU) such as optical character recognition, word spotting, enhancement etc. suffer from structural deformations like strike-outs and unconstrained strokes, to name a few. They also suffer from texture degradation due to blurring, aging, or blotting-spots etc. The DIU applications with deep networks are limited to constrained environment and lack diverse data with text-level and pixel-level annotation simultaneously. In this work, we propose a generative framework to produce realistic synthetic handwritten document images with simultaneous annotation of text and corresponding pixel-level spatial foreground information. The proposed approach generates realistic backgrounds with artificial handwritten texts which supplements data-augmentation in multiple unconstrained DIU systems. The proposed framework is an early work to facilitate DIU system-evaluation in both image quality and recognition performance at a go.

In recent years, Attribute-Based Access Control (ABAC) has become quite popular and effective for enforcing access control in dynamic and collaborative environments. Implementation of ABAC requires the creation of a set of attribute-based rules which cumulatively form a policy. Designing an ABAC policy ab initio demands a substantial amount of effort from the system administrator. Moreover, organizational changes may necessitate the inclusion of new rules in an already deployed policy. In such a case, re-mining the entire ABAC policy requires a considerable amount of time and administrative effort. Instead, it is better to incrementally augment the policy. In this paper, we propose PAMMELA, a Policy Administration Methodology using Machine Learning to assist system administrators in creating new ABAC policies as well as augmenting existing policies. PAMMELA can generate a new policy for an organization by learning the rules of a policy currently enforced in a similar organization. For policy augmentation, new rules are inferred based on the knowledge gathered from the existing rules. A detailed experimental evaluation shows that the proposed approach is both efficient and effective.

Rubber stamps often overlap with original text content of a document, and hence obscure the text regions very badly. Removal of these stamp regions becomes a necessity for successful conversion of such documents into electronic format. Stamp removal from a document becomes more difficult when they are in gray scale, or text and stamp are of the same color. In this paper, we propose a technique to remove such stamps from overlapped regions by identifying stamp regions and stamp pixels.