Cao De Tran

Document Retrieval Based on Logo Spotting Using Key-Point Matching

In this paper, we present an approach to retrieve documents based on logo spotting and recognition. A document retrieval system is proposed inspired from our previous method for logo spotting and recognition. First, the key-points from both the query logo images and a given set of document images are extracted and described by SIFT descriptor, and are matched in the SIFT feature space. They are filtered by the nearest neighbor matching rule based on the two nearest neighbors and are then post-filtered with BRIEF descriptor. Secondly, logo segmentation is performed using spatial density-based clustering, and homography is used to filter the matched key-points as a post processing. Finally, for ranking, we use two measures which are calculated based on the number of matched key-points. Tested on a well-known benchmark database of real world documents containing logos Tobacco-800, our approach achieves better performance than the state-of-the-art methods.

Text and non-text segmentation based on connected component features

Document image segmentation is crucial to OCR and other digitization processes. In this paper, we present a learning-based approach for text and non-text separation in document images. The training features are extracted at the level of connected components, a mid-level between the slow noise-sensitive pixel level, and the segmentation-dependent zone level. Given all types, shapes and sizes of connected components, we extract a powerful set of features based on size, shape, stroke width and position of each connected component. Adaboosting with Decision trees is used for labeling connected components. Finally, the classification of connected components into text and non-text is corrected based on classification probabilities and size as well as stroke width analysis of the nearest neighbors of a connected component. The performance of our approach has been evaluated on the two standard datasets: UW-III and ICDAR-2009 competition for document layout analysis. Our results demonstrate that the proposed approach achieves competitive performance for segmenting text and non-text in document images of variable content and degradation.

Improving Logo Spotting and Matching for Document Categorization by a Post-Filter Based on Homography

Digital document categorization based on logo spotting and recognition has raised a great interest in the research community because logos in documents are sources of information for categorizing documents with low costs. In this paper, we present an approach to improve the result of our method for logo spotting and recognition based on key point matching and presented in our previous paper [7]. First, the key points from both the query document images and a given set of logos (logo gallery) are extracted and described by SIFT, and are matched in the SIFT feature space. Secondly, logo segmentation is performed using spatial density-based clustering. The contribution of this paper is to add a third step where homography is used to filter the matched key points as a post-processing. And finally, in the decision stage, logo classification is performed by using an accumulating histogram. Our approach is tested using a well-known benchmark database of real world documents containing logos, and achieves good performances compared to state-of-the-art approaches.

SRIF: Scale and Rotation Invariant Features for camera-based document image retrieval

In this paper, we propose a new feature vector, named Scale and Rotation Invariant Features (SRIF), for real-time camera-based document image retrieval. SRIF is based on Locally Likely Arrangement Hashing (LLAH), which has been widely used and accepted as an efficient real-time camera-based document image retrieval method based on text. SRIF is computed based on geometrical constraints between pairs of nearest points around a keypoint. It can deal with feature point extraction errors which are introduced as a result of the camera capturing of documents. The experimental results show that SRIF outperforms LLAH in terms of retrieval accuracy and processing time.

A multi-layer approach for camera-based complex map image retrieval and spotting system

In this paper, we present a method of camera-based document image retrieval for heterogeneous-content documents using different types of features from different layers of information. We use two kinds of features in this paper (Locally Likely Arrangement Hashing - LLAH - and SIFT reduced dimensions using PCA). Then, a single hash table method is used for indexing these multiple kinds of feature vectors. In addition, we employ a technique for reducing the memory required for indexing the key points in hash table. Experimental results show that the multilayer hashing gives a high accuracy and outperforms classical methods on single layer.

Key-point matching with post-filter using SIFT and BRIEF in logo spotting

In this paper, a method to spot and recognize logos based on key-point matching is proposed. It is applied and tested on a document retrieval system. First, the pairs of matched key-points are estimated by the nearest neighbor matching rule based on the two nearest neighbors in SIFT descriptor space with Euclidean distance. Second, a post-filter with BRIEF descriptor space and hamming distance is used to re-filter the key-points which are rejected by the first step. Tested on a well-known benchmark database of real world documents containing logos Tobacco-800, our method performs an increase in the number of matched key-points of the method combined with BRIEF post-filter at the same accuracy level, and achieves a better performance than the state-of-the-art methods in the field of document retrieval.

Delaunay Triangulation-Based Features for Camera-Based Document Image Retrieval System

In this paper, we propose a new feature vector, named DElaunay TRIangulation-based Features (DETRIF), for real-time camera-based document image retrieval. DETRIF is computed based on the geometrical constraints from each pair of adjacency triangles in delaunay triangulation which is constructed from centroids of connected components. Besides, we employ a hashing-based indexing system in order to evaluate the performance of DETRIF and to compare it with other systems such as LLAH and SRIF. The experimentation is carried out on two datasets comprising of 400 heterogeneous-content complex linguistic map images (huge size, 9800 X 11768 pixels resolution) and 700 textual document images.

Camera-based document image retrieval system using local features - comparing SRIF with LLAH, SIFT, SURF and ORB

In this paper, we present camera-based document retrieval systems using various local features as well as various indexing methods. We employ our recently developed features, named Scale and Rotation Invariant Features (SRIF), which are computed based on geometrical constraints between pairs of nearest points around a keypoint. We compare SRIF with state-of-the-art local features. The experimental results show that SRIF outperforms the state-of-the-art in terms of retrieval time with 90.8% retrieval accuracy.

New spatial-organization-based scale and rotation invariant features for heterogeneous-content camera-based document image retrieval

Abstract In this paper, we extend our earlier proposed feature descriptor named Scale and Rotation Invariant Features (SRIF) and a camera-based heterogeneous-content information spotting system based on the latter. Through its capacity to manage heterogeneous content in document images, SRIF represents an extension to existing strategies such as LLAH, which are dedicated to textual document images. This paper proposes new extensions of SRIF based on geometrical constraints between pairs of nearest points around a keypoint. SRIF has built-in capabilities to deal with feature point extraction errors which are introduced in camera-captured documents. To validate our method and compare it to the state-of-the-art, we have constructed three datasets of heterogeneous-content document images, along with the corresponding ground truths. Our experiment results confirm that SRIF outperforms the state-of-the-art in terms of processing time with equal or greater recall and precision for retrieval and spotting results.

Polygon-shape-based Scale and Rotation Invariant Features for camera-based document image retrieval

The scientific problem of real-time camera-based document image retrieval is achieved by computing the image features adapted to this acquisition mode i.e. the image features which are highly discriminative even under challenging conditions of camera capture as well as which are light to be computed. In this paper, we propose new extension features to our previously proposed SRIF descriptor. The new descriptor is named as PSRIF (Polygon-shape-based Scale and Rotation Invariant Features) and makes SRIF more discriminative under challenging camera capture conditions by using least number of nearest points around the keypoint. We propose to use angles and edges of the polygon established from nearest points as additional features. To validate our extension features (PSRIF), the experimentation is carried out on two datasets comprising of 400 heterogeneous-content complex linguistic map images (huge size, 9800 × 11768 pixels resolution) and 700 textual document images. The experimental results show that our extension features (PSRIF) improve the performance of SRIF as well as PSRIF outperforms the state-of-the-art methods.