In a multi-lingual multi-script country like India, a single text line of a document page may contain words of two or more scripts. For the Optical Character Recognition of such a document page it is necessary to identify different scripts from the document. In this paper, an automatic technique for word -wise identification of English, Devnagari and Urdu scripts from a single document is propo...

We continue our study of document marking to deter illicit dissemination. An experiment we performed reveals that the distortion on the photocopy of a document is very diierent in the vertical and horizontal directions. This leads to the strategy that marks a text line both vertically using line shifting and horizontally using word shifting. A line that is marked is always accompanied by two un...

In [k]n = [k]×[k]× · · ·×[k], a line consists of the collection of points where all but one coordinate is fixed and the unfixed coordinate varies over all possibilities. We consider the problem of marking (or designating) one point on each line in [k]n so that each point in [k]n is marked either a or b times, for some fixed a or b. This is equivalent to forming a strategy for a hat guessing gam...

The increase in the use of microblogging came along with the rapid growth on short linguistic data. On the other hand deep learning is considered to be the new frontier to extract meaningful information out of large amount of raw data in an automated manner. In this study, we engaged these two emerging fields to come up with a robust language identifier on demand, namely Language Identification...

We report on our system for the shared task on discrimination of similar languages (DSL 2016). The system uses only byte representations in a deep residual network (ResNet). The system, named ResIdent, is trained only on the data released with the task (closed training). We obtain 84.88% accuracy on subtask A, 68.80% accuracy on subtask B1, and 69.80% accuracy on subtask B2. A large difference ...

We describe the first mobile app for identifying plant species using automatic visual recognition. The system – called Leafsnap – identifies tree species from photographs of their leaves. Key to this system are computer vision components for discarding non-leaf images, segmenting the leaf from an untextured background, extracting features representing the curvature of the leaf’s contour over mu...