a simple robust scaleinvariant text feature detector

In this paper we present SITT, a simple robust scaleinvariant text feature detector for document mosaicing. Digital image stitching has been studied for several decades. SIFT-Features in combination with RANSAC algorithm are established to produce good panoramas. The main problem of realtime text document stitching is the size of the feature set created by SIFT-Features. We introduce SITT-Features to solve this problem. Our experiments denote that for document images SITT-Features produce faster good results than SIFT-Features

Synthesizing a virtual imager with a large field of view and a high resolution for micromanipulation.

International audiencePhoton microscope connected with a camera is the usual imager required in micromanipulation applications. That microimager gives high resolution views, but the corresponding field of view are very narrow and do not allow the vision of the entire workfield. The classical solution consists in using multiple views imaging system: a high resolution imager for local view and a low resolution imager for global view. We are developing an alternative solution based on image mosaicing that requires only one microimager. The views from that real microimager are associated in order to achieve a virtual microimager which combines a large field of view with a high resolution

Registration and categorization of camera captured documents

Camera captured document image analysis concerns with processing of documents captured with hand-held sensors, smart phones, or other capturing devices using advanced image processing, computer vision, pattern recognition, and machine learning techniques. As there is no constrained capturing in the real world, the captured documents suffer from illumination variation, viewpoint variation, highly variable scale/resolution, background clutter, occlusion, and non-rigid deformations e.g., folds and crumples. Document registration is a problem where the image of a template document whose layout is known is registered with a test document image. Literature in camera captured document mosaicing addressed the registration of captured documents with the assumption of considerable amount of single chunk overlapping content. These methods cannot be directly applied to registration of forms, bills, and other commercial documents where the fixed content is distributed into tiny portions across the document. On the other hand, most of the existing document image registration methods work with scanned documents under affine transformation. Literature in document image retrieval addressed categorization of documents based on text, figures, etc. However, the scalability of existing document categorization methodologies based on logo identification is very limited. This dissertation focuses on two problems (i) registration of captured documents where the overlapping content is distributed into tiny portions across the documents and (ii) categorization of captured documents into predefined logo classes that scale to large datasets using local invariant features. A novel methodology is proposed for the registration of user defined Regions Of Interest (ROI) using corresponding local features from their neighborhood. The methodology enhances prior approaches in point pattern based registration, like RANdom SAmple Consensus (RANSAC) and Thin Plate Spline-Robust Point Matching (TPS-RPM), to enable registration of cell phone and camera captured documents under non-rigid transformations. Three novel aspects are embedded into the methodology: (i) histogram based uniformly transformed correspondence estimation, (ii) clustering of points located near the ROI to select only close by regions for matching, and (iii) validation of the registration in RANSAC and TPS-RPM algorithms. Experimental results on a dataset of 480 images captured using iPhone 3GS and Logitech webcam Pro 9000 have shown an average registration accuracy of 92.75% using Scale Invariant Feature Transform (SIFT). Robust local features for logo identification are determined empirically by comparisons among SIFT, Speeded-Up Robust Features (SURF), Hessian-Affine, Harris-Affine, and Maximally Stable Extremal Regions (MSER). Two different matching methods are presented for categorization: matching all features extracted from the query document as a single set and a segment-wise matching of query document features using segmentation achieved by grouping area under intersecting dense local affine covariant regions. The later approach not only gives an approximate location of predicted logo classes in the query document but also helps to increase the prediction accuracies. In order to facilitate scalability to large data sets, inverted indexing of logo class features has been incorporated in both approaches. Experimental results on a dataset of real camera captured documents have shown a peak 13.25% increase in the F–measure accuracy using the later approach as compared to the former

Processing Camera-captured Document Images: Geometric Rectification, Mosaicing, and Layout Structure Recognition

This dissertation explores three topics: 1) geometric rectification of cameracaptured document images, 2) camera-captured document mosaicing, and 3) layout structure recognition. The first two topics pertain to camera-based document image analysis, a new trend within the OCR community. Compared to typical scanners,cameras offer convenient, flexible, portable, and non-contact image capture, which enables many new applications and breathes new life into existing ones. The third topic is related to the need for efficient metadata extraction methods, critical for managing digitized documents. The kernel of our geometric rectification framework is a novel method for estimating document shape from a single camera-captured image. Our method uses texture flows detected in printed text areas and is insensitive to occlusion. Classification of planar versus curved documents is done automatically. For planar pages, we obtain full metric rectification. For curved pages, we estimate a planar-strip approximation based on properties of developable surfaces. Our method can process any planar or smoothly curved document captured from an arbitrary position without requiring 3D data, metric data, or camera calibration. For the second topic, we design a novel registration method for document images, which produces good results in difficult situations including large displacements, severe projective distortion, small overlapping areas, and lack of distinguishable feature points. We implement a selective image composition method that outperforms conventional image blending methods in overlapping areas. It eliminates double images caused by mis-registration and preserves the sharpness in overlapping areas. We solve the third topic with a graph-based model matching framework. Layout structures are modeled by graphs, which integrate local and global features and are extensible to new features in the future. Our model can handle large variation within a class and subtle differences between classes. Through graph matching, the layout structure of a document is discovered. Our layout structure recognition technique accomplishes document classification and logical component labeling at the same time. Our model learning method enables a model to adapt to changes in classes over time

Image mosaicing of panoramic images

Image mosaicing is combining or stitching several images of a scene or object taken from different angles into a single image with a greater angle of view. This is practised a developing field. Recent years have seen quite a lot of advancement in the field. Many algorithms have been developed over the years. Our work is based on feature based approach of image mosaicing. The steps in image mosaic consist of feature point detection, feature point descriptor extraction and feature point matching. RANSAC algorithm is applied to eliminate variety of mismatches and acquire transformation matrix between the images. The input image is transformed with the right mapping model for image stitching. Therefore, this paper proposes an algorithm for mosaicing two images efficiently using Harris-corner feature detection method, RANSAC feature matching method and then image transformation, warping and by blending methods

Document Image Analysis Techniques for Handwritten Text Segmentation, Document Image Rectification and Digital Collation

Document image analysis comprises all the algorithms and techniques that are utilized to convert an image of a document to a computer readable description. In this work we focus on three such techniques, namely (1) Handwritten text segmentation (2) Document image rectification and (3) Digital Collation. Offline handwritten text recognition is a very challenging problem. Aside from the large variation of different handwriting styles, neighboring characters within a word are usually connected, and we may need to segment a word into individual characters for accurate character recognition. Many existing methods achieve text segmentation by evaluating the local stroke geometry and imposing constraints on the size of each resulting character, such as the character width, height and aspect ratio. These constraints are well suited for printed texts, but may not hold for handwritten texts. Other methods apply holistic approach by using a set of lexicons to guide and correct the segmentation and recognition. This approach may fail when the domain lexicon is insufficient. In the first part of this work, we present a new global non-holistic method for handwritten text segmentation, which does not make any limiting assumptions on the character size and the number of characters in a word. We conduct experiments on real images of handwritten texts taken from the IAM handwriting database and compare the performance of the presented method against an existing text segmentation algorithm that uses dynamic programming and achieve significant performance improvement. Digitization of document images using OCR based systems is adversely affected if the image of the document contains distortion (warping). Often, costly and precisely calibrated special hardware such as stereo cameras, laser scanners, etc. are used to infer the 3D model of the distorted image which is used to remove the distortion. Recent methods focus on creating a 3D shape model based on 2D distortion informa- tion obtained from the document image. The performance of these methods is highly dependent on estimating an accurate 2D distortion grid. These methods often affix the 2D distortion grid lines to the text line, and as such, may suffer in the presence of unreliable textual cues due to preprocessing steps such as binarization. In the domain of printed document images, the white space between the text lines carries as much information about the 2D distortion as the text lines themselves. Based on this intuitive idea, in the second part of our work we build a 2D distortion grid from white space lines, which can be used to rectify a printed document image by a dewarping algorithm. We compare our presented method against a state-of-the-art 2D distortion grid construction method and obtain better results. We also present qualitative and quantitative evaluations for the presented method. Collation of texts and images is an indispensable but labor-intensive step in the study of print materials. It is an often used methodology by textual scholars when the manuscript of the text does not exist. Although various methods and machines have been designed to assist in this labor, it still remains an expensive and time- consuming process, often requiring travel to distant repositories for the painstaking visual examination of multiple original copies. Efforts to digitize collation have so far depended on first transcribing the texts to be compared, thus introducing into the process more labor and expense, and also more potential error. Digital collation will instead automate the first stages of collation directly from the document images of the original texts, thereby speeding the process of comparison. We describe such a novel framework for digital collation in the third part of this work and provide qualitative results