Extraction of Bibliography Information Based on Image of Book Cover

Yang, Hua; Kashimura, Masaaki; Onda, Norikazu; Ozawa, Shogo

doi:10.1142/s0218001400000611

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…Finally, to select best-fit metadata type for a block, their system utilized optimal matching of bipartite graph to obtain a global optimal assignment. Yang et al (1999) used image processing approach (without using OCR) to segment color space of the image of book cover. The text regions that contain header bibliography information could then be extracted.…”

Section: Introduction 11 Motivation and Related Workmentioning

confidence: 99%

Extracting bibliographical data for PDF documents with HMM and external resources

Hsiao

Chang

Thomas

2014

Program

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Purpose – The purpose of this paper is to propose an automatic metadata extraction and retrieval system to extract bibliographical information from digital academic documents in portable document formats (PDFs). Design/methodology/approach – The authors use PDFBox to extract text and font size information, a rule-based method to identify titles, and an Hidden Markov Model (HMM) to extract the titles and authors. Finally, the extracted titles and authors (possibly incorrect or incomplete) are sent as query strings to digital libraries (e.g. ACM, IEEE, CiteSeerX, SDOS, and Google Scholar) to retrieve the rest of metadata. Findings – Four experiments are conducted to examine the feasibility of the proposed system. The first experiment compares two different HMM models: multi-state model and one state model (the proposed model). The result shows that one state model can have a comparable performance with multi-state model, but is more suitable to deal with real-world unknown states. The second experiment shows that our proposed model (without the aid of online query) can achieve as good performance as other researcher's model on Cora paper header dataset. In the third experiment the paper examines the performance of our system on a small dataset of 43 real PDF research papers. The result shows that our proposed system (with online query) can perform pretty well on bibliographical data extraction and even outperform the free citation management tool Zotero 3.0. Finally, the paper conducts the fourth experiment with a larger dataset of 103 papers to compare our system with Zotero 4.0. The result shows that our system significantly outperforms Zotero 4.0. The feasibility of the proposed model is thus justified. Research limitations/implications – For academic implication, the system is unique in two folds: first, the system only uses Cora header set for HMM training, without using other tagged datasets or gazetteers resources, which means the system is light and scalable. Second, the system is workable and can be applied to extracting metadata of real-world PDF files. The extracted bibliographical data can then be imported into citation software such as endnote or refworks to increase researchers’ productivity. Practical implications – For practical implication, the system can outperform the existing tool, Zotero v4.0. This provides practitioners good chances to develop similar products in real applications; though it might require some knowledge about HMM implementation. Originality/value – The HMM implementation is not novel. What is innovative is that it actually combines two HMM models. The main model is adapted from Freitag and Mccallum (1999) and the authors add word features of the Nymble HMM (Bikel et al, 1997) to it. The system is workable even without manually tagging the datasets before training the model (the authors just use cora dataset to train and test on real-world PDF papers), as this is significantly different from what other works have done so far. The experimental results have shown sufficient evidence about the feasibility of our proposed method in this aspect.

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Section: Introduction 11 Motivation and Related Workmentioning

confidence: 99%

Extracting bibliographical data for PDF documents with HMM and external resources

Hsiao

Chang

Thomas

2014

Program

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“…The frequently used approaches are based on horizontal projection profiles (HPP) [1]- [3], piece-wise HPP [4]- [11], Hough transform [12]- [15], and blurring [16], [17]. Algorithms have been designed and proposed for different languages such as English [2], [5], [14], [16], [18], [19], Hindi [7], [10], [20]- [22], Japanese [23]- [26], Chinese [19], [27], and Arabic [3], [8], [9]. The approaches in which the whole document image is processed globally cannot extract these complicated text lines with an acceptable accuracy.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Script-Independent Text Line Extraction

Ziaratban

Faez

2011

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, an adaptive block-based text line extraction algorithm is proposed. Three global and two local parameters are defined to adapt the method to various handwritings in different languages. A document image is segmented into several overlapping blocks. The skew of each block is estimated. Text block is de-skewed by using the estimated skew angle. Text regions are detected in the de-skewed text block. A number of data points are extracted from the detected text regions in each block. These data points are used to estimate the paths of text lines. By thinning the background of the image including text line paths, text line boundaries or separators are estimated. Furthermore, an algorithm is proposed to assign to the extracted text lines the connected components which have intersections with the estimated separators. Extensive experiments on different standard datasets in various languages demonstrate that the proposed algorithm outperforms previous methods.

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“…11 Also, a system that focuses on extracting and classifying bibliographical information from book covers has been developed. 15 Many other approaches apply the features of wavelet coefficients to extract text. [1][2][3]6 All such systems concentrate on processing document images whose texts do not overlay a complex background.…”

Section: Introductionmentioning

confidence: 99%

Multi-Layer Segmentation of Complex Document Images

Chen

Chiu

2005

Int. J. Patt. Recogn. Artif. Intell.

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Text is commonly printed on a complex background. Segmenting text is an important part in document analysis. In the past some methods have been shown for the segmentation of texts with images. However, previous studies have not sufficiently addressed complex compound documents. This investigation presents an algorithm for the segmentation of text in various document images. The proposed segmentation algorithm applies a new multilayer segmentation method to separate the text from various compound document images, independent from the text and background overlapping or not. This method solves various problems associated with the complexity of background images. Experimental results obtained using various document images scanned from book covers, advertisements, brochures and magazines, reveal that the proposed algorithm can successfully segment Chinese and English text strings from various backgrounds, regardless of whether the texts are over a simple, slowly varying or rapidly varying background texture.

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Extraction of Bibliography Information Based on Image of Book Cover

Cited by 6 publications

References 13 publications

Extracting bibliographical data for PDF documents with HMM and external resources

Extracting bibliographical data for PDF documents with HMM and external resources

Adaptive Script-Independent Text Line Extraction

Multi-Layer Segmentation of Complex Document Images

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