Reading #17: Distinguishing Text from Graphics in On-line Handwritten Ink

Comments:
Kim

Summary:
What is the probability that a system can interpret your text from graphics when you draw with a stylus? Such is the question behind the work in this paper. The system described is broken into three main approaches.

1. Independent Strokes: Sequences of points between pen-down and pen-up events are taken to be strokes. 11 features are computed for each stroke. A multilayer perceptron is used to train a classifier as to which feature vectors correspond to either text or graphics.
2. Hidden Markov Model (HMM): The order of strokes can lend a clue as to what they should be classified as (unless the user jumps between a letter and a shape because they are weird). By looking at overall classification patterns, the HMM can be used to predict the current stroke givent he last stroke.
3. Bi-partite HMM: The gaps between strokes can lend additional information. A user will employ a different sized graph between two text strokes, two graphics, or a mixture therein.
   

Discussion:
I think I read this paper before... anyway! I did not like the way that they presented their results. Call me old fashioned, but I think you should always put your accuracies in plain old X (where X is your written language used). And was that plot drawn in paint? I felt like I was interpreting their findings rather than reading about them! Besides that, I thought the paper was very interesting.