James A. Herson

Image analysis for efficient categorization of image-based spam e-mail

To circumvent prevalent text-based anti-spam filters, spammers have begun embedding the advertisement text in images. Analogously, proprietary information (such as source code) may be communicated as screenshots to defeat text-based monitoring of outbound e-mail. The proposed method separates spam images from other common categories of e-mail images based on extracted overlay text and color features. No expensive OCR processing is necessary. Our method works robustly in spite of complex backgrounds, compression artifacts, and a wide variety of formats and fonts of overlaid spam text. It is also demonstrated successfully to detect screen-shots in outbound e-mail.

Rectification and recognition of text in 3-D scenes

Abstract.Real-world text on street signs, nameplates, etc. often lies in an oblique plane and hence cannot be recognized by traditional OCR systems due to perspective distortion. Furthermore, such text often comprises only one or two lines, preventing the use of existing perspective rectification methods that were primarily designed for images of document pages. We propose an approach that reliably rectifies and subsequently recognizes individual lines of text. Our system, which includes novel algorithms for extraction of text from real-world scenery, perspective rectification, and binarization, has been rigorously tested on still imagery as well as on MPEG-2 video clips in real time.

Syntax-directed content analysis of videotext: application to a map detection recognition system

Video is an increasingly important and ever-growing source of information to the intelligence and homeland defense analyst. A capability to automatically identify the contents of video imagery would enable the analyst to index relevant foreign and domestic news videos in a convenient and meaningful way. To this end, the proposed system aims to help determine the geographic focus of a news story directly from video imagery by detecting and geographically localizing political maps from news broadcasts, using the results of videotext recognition in lieu of a computationally expensive, scale-independent shape recognizer. Our novel method for the geographic localization of a map is based on the premise that the relative placement of text superimposed on a map roughly corresponds to the geographic coordinates of the locations the text represents. Our scheme extracts and recognizes videotext, and iteratively identifies the geographic area, while allowing for OCR errors and artistic freedom. The fast and reliable recognition of such maps by our system may provide valuable context and supporting evidence for other sources, such as speech recognition transcripts. The concepts of syntax-directed content analysis of videotext presented here can be extended to other content analysis systems.

Edge chain analysis for object verification

A technique for verifying an object hypothesis by comparing hypothesized edge patterns to detected edge patterns is presented. Positive evidence for a hypothesis is identified as those portions of the model edges that are found in the image, either as geometrical features (such as straight lines or circular arcs} or as linked edges of arbitrary shape. Occluded portions of a model edge are detected by analyzing junctions of image and model edge chains. Negative evidence is identified as those portions of the model edges that are not found and are not occluded. We describe the types of junctions that can occur between image and model edges and discuss a technique for identifying occluded regions based on these junctions. Preliminary results of these techniques are also presented.