With an aim to extract the structural information from the table of contents (TOC) to help develop a digital document library, the requirement of identifying/segmenting the TOC page is obvious. The objective to create a digital document library is to provide a non-labour intensive, cheap and flexible way of storing, representing and managing the paper document in electronic form to facilitate indexing, viewing, printing and extracting the intended portions. Information from the TOC pages is to be extracted for use in a document database for effective retrieval of the required pages. We present a fully automatic identification and segmentation of a table of contents (TOC) page from a scanned document.

Automated detection and segmentation of table of contents page from document images

A performance evaluation protocol for the layout analysis is discussed in this paper. In the University of Washington English Document Image Database-III, there are 1600 English document images that come with manually edited ground truth of entity bounding boxes. These bounding boxes enclose text and non-text zones, text-lines, and words. We describe a performance metric for the comparison of the detected entities and the ground truth in terms of their bounding boxes. The Document Attribute Format Specification is used as the standard data representation. The protocol is intended to serve as a model for using the UW-III database to evaluate the document analysis algorithms. A set of layout analysis algorithms which detect different entities have been tested based on the data set and the performance metric. The evaluation results are presented in this paper.

Performance evaluation of document layout analysis algorithms on the UW data set

In this paper, we give a formal definition of a document image structure representation, and formulate document image structure extraction as a partitioning problem: finding an optimal solution partitioning the set of glyphs of an input document image into a hierarchical tree structure where entities within the hierarchy at each level have similar physical properties and compatible semantic labels. We present a unified methodology that is applicable to construction of document structures at different hierarchical levels. An iterative, relaxation-like method is used to find a partitioning solution that maximizes the probability of the extracted structure. All the probabilities used in the partitioning process are estimated from an extensive training set of various kinds of measurements among the entities within the hierarchy. The offline probabilities estimated in the training then drive all decisions in the online document structure extraction. We have implemented a text line extraction algorithm using this framework.

An optimization methodology for document structure extraction on Latin character documents

This paper proposes a general-purpose method for estimating the skew angles of document images. Rather than to derive a skew angle merely from text lines, the proposed method exploits various types of visual cues of image skew available in local image regions. The visual cues are extracted by Radon transform and then outliers of them are iteratively rejected through a floating cascade. A bagging (bootstrap aggregating) estimator is finally employed to combine the estimations on the local image blocks. Our experimental results show significant improvements against the state-of-the-art methods, in terms of execution speed and estimation accuracy, as well as the robustness to short and sparse text lines, multiple different skews and the presence of nontextual objects of various types and quantities.

Skew Estimation of Document Images Using Bagging

The goal of document image structure analysis is to find an optimal solution partitioning the set of glyphs on a given document image into a hierarchical tree structure where entities within the hierarchy are associated with their physical properties and semantic labels. In this dissertation, we present a unified document image structure extraction algorithm that is probability based, where the probabilities are estimated from an extensive training set of various kinds of measurements of distances between the terminal and non-terminal entities with which the algorithm works. The off-line probabilities estimated in the training then drive all decisions in the on-line segmentation module. An iterative, relaxation-like method is used to find the partitioning solution that maximizes the joint probability. This approach can be uniformly apply to the construction of the document hierarchy at any level. We have implemented a text line segmentation algorithm and a text block extraction algorithm using this framework. Another example is the development of a system that detects and recognizes special symbols (Greek letters, mathematical symbols, etc.) on technical document pages, that are not handled by the current Optical Character Recognition (OCR) systems. 
A large quantity of ground-truth data, varying in quality, is required in order to give an accurate measurement of the performance of an algorithm under different conditions. 
We have constructed the University of Washington English Document Image Database-III, which contains 1600 scanned scientific/technical document image pages that come with manually edited ground-truth of entity bounding boxes and properties. Based on the ground-truth data, we can evaluate the performance of document analysis algorithms and build statistical models to characterize various types of document image structures. In this dissertation, we present a set of quantitative performance metrics for each kind of information a document image analysis technique infers. The text line and text block extraction algorithms were trained and evaluated on the UW-III database using a cross-validation method. The text line extraction algorithm identifies and segments 99.76% of text lines correctly, while the preliminary result of the text block extraction shows 91% accuracy.

Document structure analysis and performance evaluation

The aim of this study is to apply solid statistical methods to systematically model and extract various layout structures on document images, such as words, text lines and text blocks.
We first establish the computation theory of the recursive morphological transforms, namely the recursive erosion transform, the recursive dilation transform, the recursive opening transform, and the recursive closing transform. The transforms serve as a set of powerful tools for the document image shape analysis.
Then we describe our efforts to construct a series of carefully ground-truthed document image databases, such as the UW English document image database (I). The database offers a platform based on which we can develop, train and evaluate our document layout analysis system.
We present three sub-components of our document layout analysis system. They are the text skew estimation, the word segmentation, and the object spatial analysis:
The text skew estimation finds the text skew angle of a document image. We develop an automatic text skew estimation algorithm using the recursive opening and closing transforms. It computes the estimated text skew angles which are within 0.5$\sp\circ$ of the true text skew angles with a probability of 0.95 on real images.
The word segmentation detects all the words on a document image. We describe a word segmentation algorithm that utilizes the recursive closing transform. We derive the quantitative measures, such as the rates of miss, false, correct, splitting, merging and spurious detections, to evaluate its performance. The results show that the algorithm correctly detects the words on a document image at a rate of about 95%.
The object spatial analysis treats the detected words as atomic and employs a probabilistic linear displacement model (PLDM) and an augmented PLDM model to model and extract the text lines and text blocks in a document image. By gathering statistics from a large population of document images, we are able to validate our models and determine the proper model parameters. The correct text line and text block detection rates are about 92% and 81% respectively.

Su Chen

Papers

Document layout analysis using recursive morphological transforms