Detecting equation regions from scanned books has received attention in the document image research community in the past few years. Compared with regular text blocks, equation regions have more complicated layouts so we can not simply use text lines to model them. On the other hand, these regions consist of text symbols that can be reflowed, so that the OCR engines should parse them instead of rasterizing them like image regions. In this paper, we present an equation detector with two major contributions: (i) it is built on a simple algorithm that uses the density of special symbols, such that no additional classifier is required, (ii) it has been built into the open source Tesseract that can be accessed and used by the OCR community. The algorithm is tested on the Google Books database with 1534 entries sampled from books/magazines/newspapers of over thirty languages. And we show that Tesseract performance is improved after enabling the detector.

Although large language models are used in speech recognition and machine translation applications, OCR systems are "far behind" in their use of language models. The reason for this is not the laggardness of the OCR community, but the fact that, at high accuracies, a frequency-based language model can do more damage than good, unless carefully applied. This paper presents an analysis of this discrepancy with the help of the Google Books n-gram Corpus, and concludes that noisy-channel models that closely model the underlying classifier and segmentation errors are required.

Detecting tables in document images is important since not only do tables contain important information, but also most of the layout analysis methods fail in the presence of tables in the document image. Existing approaches for table detection mainly focus on detecting tables in single columns of text and do not work reliably on documents with varying layouts. This paper presents a practical algorithm for table detection that works with a high accuracy on documents with varying layouts (company reports, newspaper articles, magazine pages, ...). An open source implementation of the algorithm is provided as part of the Tesseract OCR engine. Evaluation of the algorithm on document images from publicly available UNLV dataset shows competitive performance in comparison to the table detection module of a commercial OCR system.

The MPEG-7 standard defines two types of texture features: texture retrieval descriptor (TRD) for retrieval and texture browsing descriptor (TBD)for browsing. The retrieval process is straightforward but it is unclear how one could use TBD for browsing. This paper describes two methods of generating layouts for browsing a texture image database. The layouts are then subject to quantitative and qualitative evaluations. The experiments showed that: (1) only some features of TBD are appropriate for browsing, (2) once the inappropriate features are removed TBD is good for browsing only if the textures are structured, (3) the layouts generated usingTRDare more suitable for browsing.

An important part of any document recognition system is detection of skew in the image of a page. This paper presents a new, accurate and robust skew detection algorithm based on a method for finding rows of text in page images. Results of a comparison of the new algorithm against Baird's well-known algorithm on 400 pages show the new algorithm to be more accurate, robust and somewhat faster. In particular, the new algorithm only breaks down at skew angles in excess of 15 degrees, compared to the almost uniform distribution of breakdowns of Baird's algorithm.