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Women in the global south often seek justice of online harassment through unavailing the harassers and the screenshots of their sent harassment texts and visual contents on social media. However, women often experience further backlash with the argument that these screenshots may have been manipulated. We studied these issues through a survey with 91 female Facebook users and interviews with 43 females and other stakeholders from Bangladesh and designed 'Unmochon'-a tool that helps women collect such harassment texts and visual contents from Facebook Messenger, share with their intended group of users and prove their authenticity. Our user-study with 48 participants revealed various challenging aspects of seeking justice on social media using such technologies and the assumptions they are built on. Based these findings, we further discuss how technologies should be designed to address women's harassment on social media in such a complex social setting.

Feature extraction is one of the fundamental problems of character recognition. The performance of character recognition system is depends on proper feature extraction and correct classifier selection. In this article, a rapid feature extraction method is proposed and named as Celled Projection (CP) that compute the projection of each section formed through partitioning an image. The recognition performance of the proposed method is compared with other widely used feature extraction methods that are intensively studied for many different scripts in literature. The experiments have been conducted using Bangla handwritten numerals along with three different well known classifiers which demonstrate comparable results including 94.12% recognition accuracy using celled projection.

Detection of blood vessels in retinal fundus image is the preliminary step to diagnose several retinal diseases. There exist several methods to automatically detect blood vessels from retinal image with the aid of different computational methods. However, all these methods require lengthy processing time. The method proposed here acquires binary vessels from a RGB retinal fundus image in almost real time. Initially, the phase congruency of a retinal image is generated, which is a softclassification of blood vessels. Phase congruency is a dimensionless quantity that is invariant to changes in image brightness or contrast; hence, it provides an absolute measure of the significance of feature points. This experiment acquires phase congruency of an image using LogGabor wavelets. To acquire a binary segmentation, thresholds are applied on the phase congruency image. The process of determining the best threshold value is based on area under the relative operating characteristic (ROC) curve. The proposed method is able to detect blood vessels in a retinal fundus image within 10 s on a PC with (accuracy, area under ROC curve) = (0.91, 0.92), and (0.92, 0.94) for the STARE and the DRIVE databases, respectively.