Suoqian Feng

Bio: Suoqian Feng is an academic researcher from Peking University. The author has contributed to research in topics: Digital image processing & Feature detection (computer vision). The author has an hindex of 1, co-authored 1 publications receiving 22 citations.

Document image retrieval based on density distribution feature and key block feature

Abstract: Document image retrieval is an important part of many document image processing systems such as paperless office systems, digital libraries and so on. Its task is to help users find out the most similar document images from a document image database. For developing a system of document image retrieval among different resolutions, different formats document images with hybrid characters of multiple languages, a new retrieval method based on document image density distribution features and key block features is proposed in this paper. Firstly, the density distribution and key block features of a document image are defined and extracted based on documents' print-core. Secondly, the candidate document images are attained based on the density distribution features. Thirdly, to improve reliability of the retrieval results, a confirmation procedure using key block features is applied to those candidates. Experimental results on a large scale document image database, which contains 10385 document images, show that the proposed method is efficient and robust to retrieve different kinds of document images in real time.

A new thresholding algorithm for document images based on the perception of objects by distance

Abstract: In this work a new method to enhance and binarize document images with several kind of degradation is proposed. The method is based on the idea that by the absolute difference between a document image and its background it is possible to effectively emphasize the text and attenuate degraded regions. To generate the background of a document our work was inspired on the human visual system and on the perception of objects by distance. Snellen's visual acuity notation was used to define how far an image must be from an observer so that the details of the characters are not perceived anymore, remaining just the background. A scheme that combines k-means clustering algorithm and Otsu's thresholding method is also used to perform binarization. The proposed method has been tested on two different datasets of document images DIBCO 2011 and a real historical document image dataset with very satisfactory results.

Document Image Retrieval: An Overview

Abstract: economic feasibility of creating a large database of document image has left a tremendous need for robust ways to access the information. Printed documents are scanned for archiving or in an attempt to move towards a paperless office and are stored as images. In this paper, we provide a survey of methods developed by researchers to access document images. The survey includes papers covering the current state of art on the research in document image retrieval based on images such as signature, logo, machine-print, different fonts etc.

Document Image Retrieval with Local Feature Sequences

Abstract: In recent years, many document image retrieval algorithms have been proposed. However, most of the current approaches either need good quality images or depend on the page layout structure. This paper presents a fast, accurate and OCR-free image retrieval algorithm using local feature sequences which can describe the intrinsic, unique and page-layout-free characteristics of document images. With a simple preprocessing step, the local feature sequences can be extracted without print-core detection and image registration. Then an efficient coarse-to-fine common substring matching strategy is applied to do local feature sequences matching. Beyond a single matching score, this approach can locate the matched parts word by word. It well handles the challenges including low resolution, different language, rotation and incompleteness and N-up. The encouraging experiment results on a large scale document image database show the retrieval outputs are sufficient good to be used directly as document image identification results.

Document Images Retrieval Based on Multiple Features Combination

Abstract: Retrieving the relevant document images from a great number of digitized pages with different kinds of artificial variations and documents quality deteriorations caused by scanning and printing is a meaningful and challenging problem. We attempt to deal with this problem by combining up multiple different kinds of document features in a hybrid way. Firstly, two new kinds of document image features based on the projection histograms and crossings number histograms of an image are proposed. Secondly, the proposed two features, together with density distribution feature and local binary pattern feature, are combined in a multistage structure to develop a novel document image retrieval system. Experimental results show that the proposed novel system is very efficient and robust for retrieving different kinds of document images, even if some of them are severely degraded.

Document image classification: Progress over two decades

Abstract: Document image classification plays a vital role in the document image processing system. Thus it is of great importance to have a clear understanding of the state-of-the-art of the document image classification field, especially in this deep learning era, which will facilitate the development of effective document image processing systems. In this paper, we provide a comprehensive survey of the progress that has been made in the field of document image classification over the past two decades. We categorize the document images into non-mobile images and mobile images according to the way they are acquired. The existing document image classification methods for these two types of images are reviewed, which are classified as textual-based methods, structural-based methods, visual-based methods and hybrid methods. We further compare the performance of different classification methods on several public benchmark datasets. Finally, we highlight some open issues and recommend promising directions for future research.