Text Line Detection Research Articles

Multiorientation/multiscript scene text detection based on projection profile analysis and graph segmentation

Textline detection in natural images has been an important problem and researchers have attempted to address this problem by grouping connected components (CCs) into clusters corresponding to textlines. However, developing bottom-up rules that work for multiorientation and/or multiscript textlines is not a simple task. In order to address this problem, we propose a framework that incorporates projection profile analysis (PPA) into the CC-based approach. Specifically, we build a graph of CCs and recursively partition the graph into subgraphs, until textline structures are detected by PPA. Although PPA has been a common technique in document image processing, it was developed for scanned documents, and we also propose a method to compute projection profiles for CCs. Experimental results show that our method is efficient and achieves better or comparable performance on conventional datasets (ICDAR 2011/2013 and MSRA-TD500), and shows promising results on a challenging dataset (ICDAR 2015 incidental text localization dataset).

Text-Line Detection in Camera-Captured Document Images Using the State Estimation of Connected Components.

Camera-based text processing has attracted considerable attention and numerous methods have been proposed. However, most of these methods have focused on the scene text detection problem and relatively little work has been performed on camera-captured document images. In this paper, we present a text-line detection algorithm for camera-captured document images, which is an essential step toward document understanding. In particular, our method is developed by incorporating state estimation (an extension of scale selection) into a connected component (CC)-based framework. To be precise, we extract CCs with the maximally stable extremal region algorithm and estimate the scales and orientations of CCs from their projection profiles. Since this state estimation facilitates a merging process (bottom-up clustering) and provides a stopping criterion, our method is able to handle arbitrarily oriented text-lines and works robustly for a range of scales. Finally, a text-line/non-text-line classifier is trained and non-text candidates (e.g., background clutters) are filtered out with the classifier. Experimental results show that the proposed method outperforms conventional methods on a standard dataset and works well for a new challenging dataset.

Baseline detection of multi-lingual unconstrained handwritten text lines

Many handwritten text recognition systems use the baseline information for better recognition of text line characters. Improper baseline detection reduces the performance of the recognition. In this paper we propose a novel baseline detection scheme for unconstrained handwritten text lines of multilingual documents. For baseline detection of a text line, at first, we detect the set of significant contour points (S-points) of the text line. Every non-singleton subsets of S-points forms a curve. The orientation invariant features of the curve determine whether the curve can construct a probable baseline of the input text line or not. It is determined by an SVM, trained using the orientation invariant features of the curves. The curves classified as probable baselines, are sorted according to their relative positions in ascending order to get the optimal baseline. We tested our method on different handwritten text lines of Bangla(Bengali), English(Roman), Kannada, Oriya, Devnagari and Persian scripts and obtained encouraging results.

A New Technique for Multi-Oriented Scene Text Line Detection and Tracking in Video

Text detection and tracking in video is challenging due to contrast, resolution and background variations, and different orientations and text movements. In addition, the presence of both caption and scene texts in video aggravates the problem because these two text types differ in characteristics significantly . This paper proposes a new technique for detecting and tracking video texts of any orientation by using spatial and temporal information, respectively. The technique explores gradient directional symmetry at component level for smoothing edge components before text detection. Spatial information is preserved by forming Delaunay triangulation in a novel way at this level, which results in text candidates. Text characteristics are then proposed in a different way for eliminating false text candidates , which results in potential text candidates. Then grouping is proposed for combining potential text candidates regardless of orientation based on the nearest neighbor criterion. To tackle the problems of multi-font and multi-sized texts, we propose multi-scale integration by a pyramid structure, which helps in extracting full text lines. Then, the detected text lines are tracked in video by matching the subgraphs of triangulation. Experimental results for text detection and tracking on our video dataset, the benchmark video datasets, and the natural scene image benchmark datasets show that the proposed method is superior to the state-of-the-art methods in terms of recall, precision , and F-measure.

Connected Component feature Analysis based Handwritten Uyghur Text Lines Detection and Separation Algorithm

This paper presents a Uyghur text-line separation algorithm based on classified connected components of Uyghur Handwritten scripts. In order to get the location of main text-lines, this paper proposes an adaptive painting and thinning algorithm. To insure the efficiency of text-lines segmentation, the post-processing of text-line detection procedures are introduced. The experimental results show that the algorithm is strongly robust for segmentation of textlines with having some skewness, touching and overlapping, and small strokes remained.

Robust method for the text line detection and splitting of overlapping text in the Latin manuscripts

The paper presents the modified method of the text lines separation in the handwritten manuscripts. Such an approach is required for the medieval text analysis, where multiple text lines overlap and are written at different angles. The proposed approach consists in dividing the bounding boxes into smaller components based on the points of the character curves intersection. The method considers the askew text lines, producing non-rectangular zones between the neighboring lines.

Implementation of Real Time Retrieval of Text from Camera/Scanned Document Image using FPGA

For the few decades, the work on computer captured/scanned-skewed document image is a major challenge for the researchers in the field of OCR. Imaging the old historic or bulky book causes skewness and distortion on the image. The proposed metric retrieval method helps in preprocessing, skeletonization, skew detection, skew correction and recognition using horizontal and vertical text line extraction of the image. Text line detection depends on the goal of digitization. Digitization is helpful in museum and library where old historic and damaged manuscripts are imaged. In the real time the image is captured using the camera fitted in FPGA kit (cyclone II). The captured image is preprocessed with the verilog code which is converted from MATLAB code using simulink HDL coder.

Gradient Vector Flow and Grouping-Based Method for Arbitrarily Oriented Scene Text Detection in Video Images

Text detection in videos is challenging due to low resolution and complex background of videos. Besides, an arbitrary orientation of scene text lines in video makes the problem more complex and challenging. This paper presents a new method that extracts text lines of any orientations based on gradient vector flow (GVF) and neighbor component grouping. The GVF of edge pixels in the Sobel edge map of the input frame is explored to identify the dominant edge pixels which represent text components. The method extracts edge components corresponding to dominant pixels in the Sobel edge map, which we call text candidates (TC) of the text lines. We propose two grouping schemes. The first finds nearest neighbors based on geometrical properties of TC to group broken segments and neighboring characters which results in word patches. The end and junction points of skeleton of the word patches are considered to eliminate false positives, which output the candidate text components (CTC). The second is based on the direction and the size of the CTC to extract neighboring CTC and to restore missing CTC, which enables arbitrarily oriented text line detection in video frame. Experimental results on different datasets, including arbitrarily oriented text data, nonhorizontal and horizontal text data, Hua's data and ICDAR-03 data (camera images), show that the proposed method outperforms existing methods in terms of recall, precision and f-measure.

A method for text line detection in natural images

Text information in natural images is very important to cross-media retrieval, index and understanding. However, its detection is challenging due to varying backgrounds, low contrast between text and non-text regions, perspective distortion and other disturbing factors. In this paper, we propose a novel text line detection method which can detect text line aligned with a straight line in any direction. It is mainly composed of three steps. In the first step, we use the maximal stable extremal region detector with dam line constraint to detect candidate text regions, we then define a similarity measurement between two regions which combines sizes, absolute distance, relative distance, contextual information and color histograms. In the second step, we propose a text line identification algorithm based on the defined similarity measurement. The algorithm firstly searches three regions as the seeds of a line, and then expands to obtain all regions in the line. In the last step, we develop a filter to remove non-text lines. The filter uses a sparse classifier based on two dictionaries which are learned from feature vectors extracted from morphological skeletons of those candidate text lines. A comparative study using two datasets shows the excellent performance of the proposed method for accurate text line detection with horizontal or arbitrary consistent orientation.

Detection of artificial and scene text in images and video frames

Textual information in images and video frames constitutes a valuable source of high-level semantics for multimedia indexing and retrieval systems. Text detection is the most crucial step in a multimedia text extraction system and although it has been extensively studied the past decade still, it does not exist a generic architecture that would work for artificial and scene text in multimedia content. In this paper we propose a system for text detection of both artificial and scene text in images and video frames. The system is based on a machine learning stage which uses an Random Forest classifier and a highly discriminative feature set produced by using a new texture operator called Multilevel Adaptive Color edge Local Binary Pattern (MACeLBP). MACeLBP describes the spatial distribution of color edges in multiple adaptive levels of contrast. Then, a gradient-based algorithm is applied to achieve distinction among text lines as well as refinement in the localization of the text lines. The whole algorithm is situated in a multiresolution framework to achieve invariance to scale for the detection of text lines. Finally, an optional connected-component step segments text lines into words based on the distances between the resulting components. The experimental results are produced by applying a concise evaluation methodology and prove the superior performance achieved by the proposed text detection system for artificial and scene text in images and video frames.

Offline handwritten Amharic word recognition

This paper describes two approaches for Amharic word recognition in unconstrained handwritten text using HMMs. The first approach builds word models from concatenated features of constituent characters and in the second method HMMs of constituent characters are concatenated to form word model. In both cases, the features used for training and recognition are a set of primitive strokes and their spatial relationships. The recognition system does not require segmentation of characters but requires text line detection and extraction of structural features, which is done by making use of direction field tensor. The performance of the recognition system is tested by a dataset of unconstrained handwritten documents collected from various sources, and promising results are obtained.

Text Line Detection and Segmentation: Uneven Skew Angles and Hill-and-Dale Writing

Text Line Detection and Segmentation: Uneven Skew Angles and Hill-and-Dale Writing

A new scheme for unconstrained handwritten text-line segmentation

Variations in inter-line gaps and skewed or curled text-lines are some of the challenging issues in segmentation of handwritten text-lines. Moreover, overlapping and touching text-lines that frequently appear in unconstrained handwritten text documents significantly increase segmentation complexities. In this paper, we propose a novel approach for unconstrained handwritten text-line segmentation. A new painting technique is employed to smear the foreground portion of the document image. The painting technique enhances the separability between the foreground and background portions enabling easy detection of text-lines. A dilation operation is employed on the foreground portion of the painted image to obtain a single component for each text-line. Thinning of the background portion of the dilated image and subsequently some trimming operations are performed to obtain a number of separating lines, called candidate line separators. By using the starting and ending points of the candidate line separators and analyzing the distances among them, related candidate line separators are connected to obtain segmented text-lines. Furthermore, the problems of overlapping and touching components are addressed using some novel techniques. We tested the proposed scheme on text-pages of English, French, German, Greek, Persian, Oriya and Bangla and remarkable results were obtained.

Semi-supervised learning for text-line detection

Automatically detecting text-lines from document images has been long studied. However, most researchers today are focusing on boosting the detection rate instead of noise removal. In this paper, we propose a semi-supervised learning framework that targets to segment Manhattan-layout documents with significant levels of noise. The algorithm consists of three steps: first, an initial segmentation process uses the seed filling algorithm; second, an iterative grouping process uses the projection profiles to estimate the vertical border of page contents; third, an inside page-content noise removal uses the online training and classification. We test our algorithm using two databases. The first is the University of Washington (UW)-III database with 1,600 images of different input qualities that has been widely used by the Document Analysis Research (DAR) communities to measure segmentation algorithm performance. The second is the NILE database created by sampling from 320 journals pages of east Asian, east European and middle Eastern languages. The result shows that our framework achieves competitive performance in terms of both page frame level segmentation and text-line level segmentation, and is particularly strong at filtering noise. It also shows that our algorithm is more adaptive to language variations.

Text line and word segmentation of handwritten documents

In this paper, we present a segmentation methodology of handwritten documents in their distinct entities, namely, text lines and words. Text line segmentation is achieved by applying Hough transform on a subset of the document image connected components. A post-processing step includes the correction of possible false alarms, the detection of text lines that Hough transform failed to create and finally the efficient separation of vertically connected characters using a novel method based on skeletonization. Word segmentation is addressed as a two class problem. The distances between adjacent overlapped components in a text line are calculated using the combination of two distance metrics and each of them is categorized either as an inter- or an intra-word distance in a Gaussian mixture modeling framework. The performance of the proposed methodology is based on a consistent and concrete evaluation methodology that uses suitable performance measures in order to compare the text line segmentation and word segmentation results against the corresponding ground truth annotation. The efficiency of the proposed methodology is demonstrated by experimentation conducted on two different datasets: (a) on the test set of the ICDAR2007 handwriting segmentation competition and (b) on a set of historical handwritten documents.

Text line detection in handwritten documents

In this paper, we present a new text line detection method for handwritten documents. The proposed technique is based on a strategy that consists of three distinct steps. The first step includes image binarization and enhancement, connected component extraction, partitioning of the connected component domain into three spatial sub-domains and average character height estimation. In the second step, a block-based Hough transform is used for the detection of potential text lines while a third step is used to correct possible splitting, to detect text lines that the previous step did not reveal and, finally, to separate vertically connected characters and assign them to text lines. The performance evaluation of the proposed approach is based on a consistent and concrete evaluation methodology.

Morphology-based text line extraction

This paper presents a morphology-based text line extraction algorithm for extracting text regions from cluttered images. First of all, the method defines a novel set of morphological operations for extracting important contrast regions as possible text line candidates. The contrast feature is robust to lighting changes and invariant against different image transformations like image scaling, translation, and skewing. In order to detect skewed text lines, a moment-based method is then used for estimating their orientations. According to the orientation, an x-projection technique can be applied to extract various text geometries from the text-analogue segments for text verification. However, due to noise, a text line region is often fragmented to different pieces of segments. Therefore, after the projection, a novel recovery algorithm is then proposed for recovering a complete text line from its pieces of segments. After that, a verification scheme is then proposed for verifying all extracted potential text lines according to their text geometries. Experimental results show that the proposed method improves the state-of-the-art work in terms of effectiveness and robustness for text line detection.

An architecture for handwritten text recognition systems

This paper presents an end-to-end system for reading handwritten page images. Five functional modules included in the system are introduced in this paper: (i) pre-processing, which concerns introducing an image representation for easy manipulation of large page images and image handling procedures using the image representation; (ii) line separation, concerning text line detection and extracting images of lines of text from a page image; (iii) word segmentation, which concerns locating word gaps and isolating words from a line of text image obtained efficiently and in an intelligent manner; (iv) word recognition, concerning handwritten word recognition algorithms; and (v) linguistic post-pro- cessing, which concerns the use of linguistic constraints to intelligently parse and recognize text. Key ideas employed in each functional module, which have been developed for dealing with the diversity of handwriting in its various aspects with a goal of system reliability and robustness, are described in this paper. Preliminary experiments show promising results in terms of speed and accuracy.