Document Image Retrieval System Research Articles

Enhancing Document Image Retrieval in Education: Leveraging Ensemble-Based Document Image Retrieval Systems for Improved Precision

Document image retrieval (DIR) systems simplify access to digital data within printed documents by capturing images. These systems act as bridges between print and digital realms, with demand in organizations handling both formats. In education, students use DIR to access online materials, clarify topics, and find solutions in printed textbooks by photographing content with their phones. DIR excels in handling complex figures and formulas. We propose using ensembles of DIR systems instead of single-feature models to enhance DIR’s efficacy. We introduce “Vote-Based DIR” and “The Strong Decision-Based DIR”. These ensembles combine various techniques, like optical code reading, spatial analysis, and image features, improving document retrieval. Our study, using a dataset of university exam preparation materials, shows that ensemble DIR systems outperform individual ones, promising better accuracy and efficiency in digitizing printed content, which is especially beneficial in education.

CTC token parsing algorithm using keyword spotting for BLSTM based unconstrained handwritten recognition

The images are becoming more popular in today’s world and being made available over the internet, scanned/captured documents are used in paperless offices and digital libraries. The keyword spotting is well known techniques in document image retrieval system and Recognition free Information retrieval is based on Keyword Spotting technique it searches for most related keyword from image as per user request by using only image features. In this paper, to avoid vanishing gradient problem in the handwritten recognition using parser methods and also character matching to perform efficient and reliable output result.

Fingerprint-Based Document Image Retrieval

Most of the documents use fingerprint impression for authentication. Property related documents, bank checks, application forms, etc., are the examples of such documents. Fingerprint-based document image retrieval system aims to provide a solution for searching and browsing of such digitized documents. The major challenges in implementing fingerprint-based document image retrieval are an efficient method for fingerprint detection and an effective feature extraction method. In this work, we propose a method for automatic detection of a fingerprint from given query document image employing Discrete Wavelet Transform (DWT)-based features and SVM classifier. In this paper, we also propose and investigate two feature extraction schemes, DWT and Stationary Wavelet Transform (SWT)-based Local Binary Pattern (LBP) features for fingerprint-based document image retrieval. The standardized Euclidean distance is employed for matching and ranking of the documents. Proposed method is tested on a database of 1200 document images and is also compared with current state-of-art. The proposed scheme provided 98.87% of detection accuracy and 73.08% of Mean Average Precision (MAP) for document image retrieval.

A comparative study of different texture features for document image retrieval

Due to the rapid increase of different digitised documents, there has been significant attention dedicated to document image retrieval over the past two decades. Finding discriminative and effective features is a fundamental task for providing a fast and more accurate retrieval system. Texture features are generally fast to compute and are suitable for large volume data. Thus, in this study, the effectiveness of texture features widely used in the literature of content-based image retrieval is investigated on document images. Twenty-six different texture feature extraction methods from four main categories of texture features, statistical, transform, model, and structural-based approaches, are considered in this research work to compare their performance on the problem of document image retrieval. Three document image datasets, MTDB, ITESOFT, and CLEF_IP with various content and page layouts are used to evaluate the twenty-six texture-based features on document image retrieval systems. The retrieval results are computed in terms of precision, recall and F-score, and a comparative analysis of the results is also provided. Feature dimensions and time complexity of the texture-based feature methods are further compared. Finally, some conclusions are drawn and suggestions are made about future research directions.

A novel shape descriptor based on salient keypoints detection for binary image matching and retrieval

We introduce a shape descriptor that extracts keypoints from binary images and automatically detects the salient ones among them. The proposed descriptor operates as follows: First, the contours of the image are detected and an image transformation is used to generate background information. Next, pixels of the transformed image that have specific characteristics in their local areas are used to extract keypoints. Afterwards, the most salient keypoints are automatically detected by filtering out redundant and sensitive ones. Finally, a feature vector is calculated for each keypoint by using the distribution of contour points in its local area. The proposed descriptor is evaluated using public datasets of silhouette images, handwritten math expressions, hand-drawn diagram sketches, and noisy scanned logos. Experimental results show that the proposed descriptor compares strongly against state of the art methods, and that it is reliable when applied on challenging images such as fluctuated handwriting and noisy scanned images. Furthermore, we integrate our descriptor in a content-based document image retrieval system using sketch queries as a step for query and candidate occurrence matching, and we show that it leads to a significant boost in retrieval performances.

Automatic logo based document image retrieval

Recently, there is need for the automatic logo detection and document image retrieval because of increasing requirements of intelligent document images. In this work, we have developed an automatic logo based document image retrieval system in which we first proposed an automatic logo detection and extraction from the document images using discrete wavelet transform (DWT). Then, we have proposed an approach to retrieve document images based on the logo using combined rotated complex wavelet filters (RCWF) and dual tree complex wavelet transform (DT-CWT). Since these combined features give information in twelve different directions. Our approach is segmentation free. Finally, we evaluate the effectiveness of our approach using large database collections of real-world complex documents.

The XML based Electronic Document Image Retrieval System

The amount of multimedia information has been increasing by the rapid growth of the Internet technology. Recently the need for the efficient image retrieval technology has been raised since an electronic document includes a significant amount of image information. In this paper, we designed and developed the information retrieval system for images and tables included in an electronic document by converting an electronic document to XML. The method proposed in this paper represents the information of images and tables in the XML format. Therefore the system could be implemented conveniently using the commercial system supporting XML and increased the retrieval accuracy for images and tables.

Exploiting Multimedia Content: A Machine Learning Based Aproach

This thesis explores use of machine learning for multimedia content management involving single/multiple features, modalities and concepts. We introduce shape based feature for binary patterns and apply it for recognition and retrieval application in single and multiple feature based architecture. The multiple feature based recognition and retrieval frameworks are based on the theory of multiple kernel learning (MKL). A binary pattern recognition framework is presented by combining the binary MKL classifiers using a decision directed acyclic graph. The evaluation is shown for Indian script character recognition, and MPEG7 shape symbol recognition. A word image based document indexing framework is presented using the distance based hashing (DBH) defined on learned pivot centres. We use a new multi-kernel learning scheme using a Genetic Algorithm for developing a kernel DBH based document image retrieval system. The experimental evaluation is presented on document collections of Devanagari, Bengali and English scripts. Next, methods for document retrieval using multi-modal information fusion are presented. Text/Graphics segmentation framework is presented for documents having a complex layout. We present a novel multi-modal document retrieval framework using the segmented regions. The approach is evaluated on English magazine pages. A document script identification framework is presented using decision level aggregation of page, paragraph and word level prediction. Latent Dirichlet Allocation based topic modelling with modified edit distance is introduced for the retrieval of documents having recognition inaccuracies. A multi-modal indexing framework for such documents is presented by a learning based combination of text and image based properties. Experimental results are shown on Devanagari script documents. Finally, we have investigated concept based approaches for multimedia analysis. A multi-modal document retrieval framework is presented by combining the generative and discriminative modelling for exploiting the cross-modal correlation between modalities. The combination is also explored for semantic concept recognition using multi-modal components of the same document, and different documents over a collection. An experimental evaluation of the framework is shown for semantic event detection in sport videos, and semantic labelling of components of multi-modal document images.

Document Image Retrieval Based on Keyword Spotting Using Relevance Feedback

Keyword Spotting is a well-known method in document image retrieval which is based on query word image. In this paper, a document image retrieval system based on keyword spotting and relevance feedback is presented. Relevance feedback as an interactive method is used in this paper to improve the performance of Document Image Retrieval System (DIRS). In the proposed method, we compare several strategies of positive and negative feedbacks which include Only Feedback, Only Negative Feedback and Positive and Negative Feedback. Experiments show that using relevance feedback in DIR outperforms common DIR.

A Kannada Document Image Retrieval System based on Correlation Method

present growth of digitization of books and manuscripts demands an immediate solution to access them electronically. This requires research in the area of document image understanding, specifically in the area of document image retrieval. There is an immense scope for such a retrieval system for a digital library of Kannada Document Images. This paper presents an efficient Content Based Document Image Retrieval system for a Kannada document image collection. A recognition-free approach is followed because recognition based approach is inefficient in terms of performance. The data is pre-processed and segmented for faster matching and retrieval. An efficient search technique - Correlation method is used to search in large collection of document images. Performance evaluation using different datasets of Kannada documents shows the effectiveness of the approach. Keywordsent based image retrieval, Correlation coefficient, Median Filtering, Kannada Document Images, Segmentation.