As the rapid growth of PDF documents, recognizing the document structure and components are useful for document storage, classification and retrieval. Table, a ubiquitous document component, becomes an important information source. Accurately detecting the table boundary plays a crucial role for many applications, e.g., the increasing demand on the table data search. Rather than converting PDFs to image or HTML and then processing with other techniques (e.g., OCR), extracting and analyzing texts from PDFs directly is easy and accurate. However, text extraction tools face a common problem: text sequence error. In this paper, we propose two algorithms to recover the sequence of extracted sparse lines, which improve the table content collection. The experimental results show the comparison of the performance of both algorithms, and demonstrate the effectiveness of text sequence recovering for the table boundary detection.

Due to the continuous and rampant increase in the size of domain specific data sources, there is a real and sustained need for fast processing in time-sensitive applications, such as medical record information extraction at the point of care, genetic feature extraction for personalized treatment, as well as off-line knowledge discovery such as creating evidence based medicine. Since parallel multi-string matching is at the core of most data mining tasks in these applications, faster on-line matching in static and streaming data is needed to improve the overall efficiency of such knowledge discovery. To solve this data mining need not efficiently handled by traditional information extraction and retrieval techniques, we propose a Block Suffix Shifting-based approach, which is an improvement over the state of the art multi-string matching algorithms such as Aho-Corasick, Commentz-Walter, and Wu-Manber. The strength of our approach is its ability to exploit the different block structures of domain specific data for off-line and online parallel matching. Experiments on several real world datasets show how our approach translates into significant performance improvements.

Most prior work on information extraction has focused on extracting information from text in digital documents. However, often, the most important information being reported in an article is presented in tabular form in a digital document. If the data reported in tables can be extracted and stored in a database, the data can be queried and joined with other data using database management systems. In order to prepare the data source for table search, accurately detecting the table boundary plays a crucial role for the later table structure decomposition. Table boundary detection and content extraction is a challenging problem because tabular formats are not standardized across all documents. In this paper, we propose a simple but effective preprocessing method to improve the table boundary detection performance by considering the sparse-line property of table rows. Our method easily simplifies the table boundary detection problem into the sparse line analysis problem with much less noise. We design eight line label types and apply two machine learning techniques, Conditional Random Field (CRF) and Support Vector Machines (SVM), on the table boundary detection field. The experimental results not only compare the performances between the machine learning methods and the heuristics-based method, but also demonstrate the effectiveness of the sparse line analysis in the table boundary detection.

As the rapid growth of PDF document in digital libraries, recognizing the document structure and detecting specific document components are useful for document storage, classification and retrieval. Tables, as a specific document component, are ubiquitous everywhere. Accurately detecting the table boundary plays a crucial role for the later table structure decomposition and table data collection. In this paper, we propose an easy but effective table boundary detection method. Our method has two unique advantages comparing with other works in this field: 1) Because most tables are text-based, we claim that the text object of PDF itself is good enough for table detection. In addition, we believe that the font information is not so reliable as other works stated. 2) Based on the nature of the table cells, we notice the sparse-line property of table rows. By filtering out the non-sparse lines initially, the table boundary detection problem can be simplified into the sparse line analysis problem easily. The experimental results not only confirm the importance of the coordinate information, but also demonstrate the effectiveness of sparse lines in the table boundary detection. Combining with other keywords, our method is even applicable to detect other document components (e.g., mathematical formula or the references).

Since nearly all information is now created digitally, large text databases have become more prevalent than ever. Automatically mining information from these databases proves to be a challenge due to slow pattern/string matching techniques. In this paper we introduce a new, fast multi-string pattern matching method called the Block Suffix Shifting (BSS) algorithm, which is based on the well known Aho-Chorasick algorithm. The advantages of our algorithm include: the ability to exploit the natural structure of text, perform significant character shifting, avoid useless backtracking jumps, efficient matching time and avoid the typical “sub-string” false positive errors. Our algorithm is applicable to many fields with free text, such as the health care domain and the scientific document field. In this paper, we apply the BSS algorithm to health care data and mine hundreds of thousands of medical concepts from a large Electronic Medical Record (EMR) corpora simultaneously and efficiently. Experimental results show the superiority of our algorithm when compared with the top of the line multi-string matching algorithms (the Aho-Corasick and the Wu-Manber algorithm).

Tables are ubiquitous. In scientific documents, tables are widely used to present experimental results or statistical data in a condensed fashion. Current search engines do not allow the end-user to search for relevant tables. In this paper, we describe TableSeer, an automatic table extraction and search engine system. TableSeer crawls scientific documents, identifies documents with tables, extracts tables from documents, indexes them and enables end-users to search for tables. We also propose an extensive set of medium-independent metadata for tables representation. Given a query, TableSeer ranks the returned results using an innovative ranking algorithm - TableRank. Our results show that TableSeer outperforms popular search engines, such as Google Scholar when the end-user seeks for tables.

Tables are ubiquitous. Unfortunately, no search engine supportstable search. In this paper, we propose a novel table specificsearching engine, TableSeer, to facilitate the table extracting, indexing, searching, and sharing. In addition, wepropose an extensive set of medium-independent metadata to precisely present tables. Given a query, TableSeer ranks the returned results using an innovative ranking algorithm - TableRank with a tailored vector space model and a novel term weightingscheme. Experimental results show that TableSeer outperforms existing search engines on table search. In addition, incorporating multiple weighting factors can significantly improve the ranking results.