Learning Visual Shape Lexicon for Document Image Content Recognition

Abstract

Developing effective content recognition methods for diverse imagery continues to challenge computer vision researchers. We present a new approach for document image content categorization using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant shape feature that is generic enough to be detected repeatably and segmentation free. We learn a concise, structurally indexed shape lexicon from training by clustering and partitioning feature types through graph cuts. We demonstrate our approach on two challenging document image content recognition problems: 1) The classification of 4, 500 Web images crawled from Google Image Search into three content categories-pure image, image with text, and document image, and 2) Language identification of 8 languages (Arabic, Chinese, English, Hindi, Japanese, Korean, Russian, and Thai) on a 1, 512 complex document image database composed of mixed machine printed text and handwriting. Our approach is capable to handle high intra-class variability and shows results that exceed other state-of-the-art approaches, allowing it to be used as a content recognizer in image indexing and retrieval systems.

Language identification for handwritten document images using a shape codebook

“…Portions of this paper appeared in previous conference publications [25,32]. This research was supported by the US Department of Defense under contract MDA-9040-2C-0406.…”

“…A codebook provides a concise structural organization for associating large varieties of lowlevel features [25], and is efficient because it enables comparison to much fewer feature types.…”

“…Our approach is based on the view that the intricate differences between languages can be effectively captured using low-level segmentation-free shape features and structurally indexed shape descriptors [25]. Low-level local shape features serve well suited for this purpose because they can be detected robustly in practice, without detection or segmentation of high-level entities, such as text lines or words.…”