Stable text line detection

Text line segmentation in freestyle handwritten documents remains an open document analysis problem. Curvilinear text lines and small gaps between neighbouring text lines present a challenge to algorithms developed for machine-printed or hand-printed documents. We investigate a general-purpose, knowledge-free method for the automatic detection of text lines based on a stable path approach. Lines affected by curvature and inclination are robustly detected. The proposed methodology was tested on a modern set of handwritten images made available on the ICDAR 2009 handwriting segmentation competition, with promissing results. © 2009 IEEE

Extracting generic text information from images

University of Technology, Sydney. Faculty of Engineering and Information Technology.As a vast amount of text appears everywhere, including natural scene, web pages and videos, text becomes very important information for different applications. Extracting text information from images and video frames is the first step of applying them to a specific application and this task is completed by a text information extraction (TIE) system. TIE consists of text detection, text binarisation and text recognition. For different applications or projects, one or more of these three TIE components may be embedded. Although many efforts have been made to extract text from images and videos, this problem is far from being solved due to the difficulties existing in different scenarios. This thesis focuses on the research of text detection and text binarisation. For the work on text detection in born-digital images, a new scheme for coarse text detection and a texture-based feature for fine text detection are proposed. In the coarse detection step, a novel scheme based on Maximum Gradient Difference (MGD) response of text lines is proposed. MGD values are classified into multiple clusters by a clustering algorithm to create multiple layer images. Then, the text line candidates are detected in different layer images. An SVM classifier trained by a novel texture-based feature is utilized to filter out the non-text regions. The superiority of the proposed feature is demonstrated by comparing with other features for text/non-text classification capability. Another algorithm is designed for detecting texts from natural scene images. Maximally Stable Extremal Regions (MSERs) as character candidates are classified into character MSERs and non-character MSERs based on geometry-based, stroke-based, HOG-based and colour-based features. Two types of misclassified character MSERs are retrieved by two different schemes respectively. A false alarm elimination step is performed for increasing the text detection precision and the bootstrap strategy is used to enhance the power of suppressing false positives. Both promising recall rate and precision rate are achieved. In the aspect of text binarisation research, the combination of the selected colour channel image and graph-based technique are explored firstly. The colour channel image with the histogram having the biggest distance, estimated by mean-shift procedure, between the two main peaks is selected before the graph model is constructed. Then, Normalised cut is employed on the graph to get the binarisation result. For circumventing the drawbacks of the grayscale-based method, a colour-based text binarisation method is proposed. A modified Connected Component (CC)-based validation measurement and a new objective segmentation evaluation criterion are applied as sequential processing. The experimental results show the effectiveness of our text binarisation algorithms

Fused Text Segmentation Networks for Multi-oriented Scene Text Detection

In this paper, we introduce a novel end-end framework for multi-oriented scene text detection from an instance-aware semantic segmentation perspective. We present Fused Text Segmentation Networks, which combine multi-level features during the feature extracting as text instance may rely on finer feature expression compared to general objects. It detects and segments the text instance jointly and simultaneously, leveraging merits from both semantic segmentation task and region proposal based object detection task. Not involving any extra pipelines, our approach surpasses the current state of the art on multi-oriented scene text detection benchmarks: ICDAR2015 Incidental Scene Text and MSRA-TD500 reaching Hmean 84.1% and 82.0% respectively. Morever, we report a baseline on total-text containing curved text which suggests effectiveness of the proposed approach.Comment: Accepted by ICPR201

Efficient Scene Text Localization and Recognition with Local Character Refinement

An unconstrained end-to-end text localization and recognition method is presented. The method detects initial text hypothesis in a single pass by an efficient region-based method and subsequently refines the text hypothesis using a more robust local text model, which deviates from the common assumption of region-based methods that all characters are detected as connected components. Additionally, a novel feature based on character stroke area estimation is introduced. The feature is efficiently computed from a region distance map, it is invariant to scaling and rotations and allows to efficiently detect text regions regardless of what portion of text they capture. The method runs in real time and achieves state-of-the-art text localization and recognition results on the ICDAR 2013 Robust Reading dataset

An Extended Stable Marriage Problem Algorithm for Clone Detection

Code cloning negatively affects industrial software and threatens intellectual property. This paper presents a novel approach to detecting cloned software by using a bijective matching technique. The proposed approach focuses on increasing the range of similarity measures and thus enhancing the precision of the detection. This is achieved by extending a well-known stable-marriage problem (SMP) and demonstrating how matches between code fragments of different files can be expressed. A prototype of the proposed approach is provided using a proper scenario, which shows a noticeable improvement in several features of clone detection such as scalability and accuracy.Comment: 20 pages, 10 figures, 6 table

Rotation-invariant features for multi-oriented text detection in natural images.

Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes