Document layout analysis

About: Document layout analysis is a research topic. Over the lifetime, 1462 publications have been published within this topic receiving 34021 citations.

Texture sparseness for pixel classification of business document images

Abstract: Contemporary business documents contain diverse, multi-layered mixtures of textual, graphical, and pictorial elements. Existing methods for document segmentation and classification do not handle well the complexity and variety of contents, geometric layout, and elemental shapes. This paper proposes a novel document image classification approach that distributes individual pixels into four fundamental classes (text, image, graphics, and background) through support vector machines. This approach uses a novel low-dimensional feature descriptor based on textural properties. The proposed feature vector is constructed by considering the sparseness of the document image responses to a filter bank on a multi-resolution and contextual basis. Qualitative and quantitative evaluations on business document images show the benefits of adopting a contextual and multi-resolution approach. The proposed approach achieves excellent results; it is able to handle varied contents and complex document layouts, without imposing any constraint or making assumptions about the shape and spatial arrangement of document elements.

Document segmentation using Relative Location Features

Abstract: In this paper we evaluate the use of Relative Location Features (RLF) on a historical document segmentation task, and compare the quality of the results obtained on structured and unstructured documents using RLF and not using them. We prove that using these features improve the final segmentation on documents with a strong structure, while their application on unstructured documents does not show significant improvement. Although this paper is not focused on segmenting unstructured documents, results obtained on a benchmark dataset are equal or even overcome previous results of similar works.

Stochastic attribute grammar model of document production and its use in document image decoding

Abstract: Document image decoding (DID) refers to the process of document recognition within a communication theory framework. In this framework, a logical document structure is a message communicated by encoding the structure as an ideal image, transmitting the ideal image through a noisy channel, and decoding the degraded image into a logical structure as close to the original message as possible, on average. Thus document image decoding is document image recognition where the recognizer performs optimal reconstruction by explicitly modeling the source of logical structures, the encoding procedure, and the channel noise. In previous work, we modeled the source and encoder using probabilistic finite-state automata and transducers. In this paper, we generalize the source and encoder models using context-free attribute grammars. We employ these models in a document image decoder that uses a dynamic programming algorithm to minimize the probability of error between original and reconstructed structures. The dynamic programming algorithm is a generalization of the Cocke-Younger-Kasami parsing algorithm.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Heuristic classification of office documents

Abstract: Document Processing Systems (DPSs) support office workers to manage information. Document classification is a major function of DPSs. By analyzing a document’s layout and conceptual structures, we present in this paper a sample-based approach to document classification. We represent a document’s layout structure by an ordered labeled tree through a procedure known as nested segmentation and represent the document’s conceptual structure by a set of attribute type pairs. The layout similarities between the document to be classified and sample documents are determined by a previously developed approximate tree matching toolkit. The conceptual similarities between the documents are determined by analyzing their contents and by calculating the degree of conceptual closeness. The document type is identified by computing both the layout and conceptual similarities between the document to be classified and the samples in the document sample base. Some experimental results are presented, which demonstrate the effectiveness of the proposed techniques.

Using domain knowledge to derive the logical structure of documents

Abstract: An important aspect of document understanding is document logical structure derivation, which involves knowledge-based analysis of document images to derive a symbolic description of their structure and contents. Domain-specific as well as generic knowledge about document layout is used in order to classify, logically group, and determine the read-order of the individual blocks in the image, i.e., translate the physical structure of the document into a layout-independent logical structure. We have developed a computational model for the derivation of the logical structure of documents. Our model uses a rule-based control structure, as well as a hierarchical multi-level knowledge representation scheme in which knowledge about various types of documents is encoded into a document knowledge base and is used by reasoning processes to make inferences about the document. An important issue addressed in our research is the kind of domain knowledge that is required for such analysis. A document logical structure derivation system (DeLoS) has been developed based on the above model, and has achieved good results in deriving the logical structure of complex multi- articled documents such as newspaper pages. Applications of this approach include its use in information retrieval from digital libraries, as well as in comprehensive document understanding systems.