Shijian Lu

Document image binarization using background estimation and stroke edges

Document images often suffer from different types of degradation that renders the document image binarization a challenging task. This paper presents a document image binarization technique that segments the text from badly degraded document images accurately. The proposed technique is based on the observations that the text documents usually have a document background of the uniform color and texture and the document text within it has a different intensity level compared with the surrounding document background. Given a document image, the proposed technique first estimates a document background surface through an iterative polynomial smoothing procedure. Different types of document degradation are then compensated by using the estimated document background surface. The text stroke edge is further detected from the compensated document image by using L1-norm image gradient. Finally, the document text is segmented by a local threshold that is estimated based on the detected text stroke edges. The proposed technique was submitted to the recent document image binarization contest (DIBCO) held under the framework of ICDAR 2009 and has achieved the top performance among 43 algorithms that are submitted from 35 international research groups.

Binarization of historical document images using the local maximum and minimum

This paper presents a new document image binarization technique that segments the text from badly degraded historical document images. The proposed technique makes use of the image contrast that is defined by the local image maximum and minimum. Compared with the image gradient, the image contrast evaluated by the local maximum and minimum has a nice property that it is more tolerant to the uneven illumination and other types of document degradation such as smear. Given a historical document image, the proposed technique first constructs a contrast image and then detects the high contrast image pixels which usually lie around the text stroke boundary. The document text is then segmented by using local thresholds that are estimated from the detected high contrast pixels within a local neighborhood window. The proposed technique has been tested over the dataset that is used in the recent Document Image Binarization Contest (DIBCO) 2009. Experiments show its superior performance.

Robust Document Image Binarization Technique for Degraded Document Images

Segmentation of text from badly degraded document images is a very challenging task due to the high inter/intra-variation between the document background and the foreground text of different document images. In this paper, we propose a novel document image binarization technique that addresses these issues by using adaptive image contrast. The adaptive image contrast is a combination of the local image contrast and the local image gradient that is tolerant to text and background variation caused by different types of document degradations. In the proposed technique, an adaptive contrast map is first constructed for an input degraded document image. The contrast map is then binarized and combined with Cannys edge map to identify the text stroke edge pixels. The document text is further segmented by a local threshold that is estimated based on the intensities of detected text stroke edge pixels within a local window. The proposed method is simple, robust, and involves minimum parameter tuning. It has been tested on three public datasets that are used in the recent document image binarization contest (DIBCO) 2009 & 2011 and handwritten-DIBCO 2010 and achieves accuracies of 93.5%, 87.8%, and 92.03%, respectively, that are significantly higher than or close to that of the best-performing methods reported in the three contests. Experiments on the Bickley diary dataset that consists of several challenging bad quality document images also show the superior performance of our proposed method, compared with other techniques.

Accurate and Efficient Optic Disc Detection and Segmentation by a Circular Transformation

Under the framework of computer-aided diagnosis, this paper presents an accurate and efficient optic disc (OD) detection and segmentation technique. A circular transformation is designed to capture both the circular shape of the OD and the image variation across the OD boundary simultaneously. For each retinal image pixel, it evaluates the image variation along multiple evenly-oriented radial line segments of specific length. The pixels with the maximum variation along all radial line segments are determined, which can be further exploited to locate both the OD center and the OD boundary accurately. Experiments show that OD detection accuracies of 99.75%, 97.5%, and 98.77% are obtained for the STARE dataset, the ARIA dataset, and the MESSIDOR dataset, respectively, and the OD center error lies around six pixels for the STARE dataset and the ARIA dataset which is much smaller than that of state-of-the-art methods ranging 14-29 pixels. In addition, the OD segmentation accuracies of 93.4% and 91.7% are obtained for STARE dataset and ARIA dataset, respectively, that consists of many severely degraded images of pathological retinas that state-of-the-art methods cannot segment properly. Furthermore, the algorithm runs in 5 s, which is substantially faster than many of the state-of-the-art methods.

ICDAR 2015 competition on Robust Reading

Results of the ICDAR 2015 Robust Reading Competition are presented. A new Challenge 4 on Incidental Scene Text has been added to the Challenges on Born-Digital Images, Focused Scene Images and Video Text. Challenge 4 is run on a newly acquired dataset of 1,670 images evaluating Text Localisation, Word Recognition and End-to-End pipelines. In addition, the dataset for Challenge 3 on Video Text has been substantially updated with more video sequences and more accurate ground truth data. Finally, tasks assessing End-to-End system performance have been introduced to all Challenges. The competition took place in the first quarter of 2015, and received a total of 44 submissions. Only the tasks newly introduced in 2015 are reported on. The datasets, the ground truth specification and the evaluation protocols are presented together with the results and a brief summary of the participating methods.

Unsupervised Brain Computer Interface Based on Intersubject Information and Online Adaptation

Conventional brain computer interfaces rely on a guided calibration procedure to address the problem of considerable variations in electroencephalography (EEG) across human subjects. This calibration, however, implies inconvenience to the end users. In this paper, we propose an online-adaptive-learning method to address this problem for P300-based brain computer interfaces. By automatically capturing subject-specific EEG characteristics during online operation, this method allows a new user to start operating a P300-based brain-computer interface without guided (supervised) calibration. The basic principle is to first learn a generic model termed subject-independent model offline from EEG of a pool of subjects to capture common P300 characteristics. For a new user, a new model termed subject-specific model is then adapted online based on EEG recorded from the new subject and the corresponding labels predicted by either the subject-independent model or the adapted subject-specific model, depending on a confidence score. To verify the proposed method, a study involving 10 healthy subjects is carried out and positive results are obtained. For instance, after 2-4 min online adaptation (spelling of 10-20 characters), the accuracy of the adapted model converges to that of a fully trained supervised subject-specific model.

Document Image Retrieval through Word Shape Coding

This paper presents a document retrieval technique that is capable of searching document images without optical character recognition (OCR). The proposed technique retrieves document images by a new word shape coding scheme, which captures the document content through annotating each word image by a word shape code. In particular, we annotate word images by using a set of topological shape features including character ascenders/descenders, character holes, and character water reservoirs. With the annotated word shape codes, document images can be retrieved by either query keywords or a query document image. Experimental results show that the proposed document image retrieval technique is fast, efficient, and tolerant to various types of document degradation.

Automatic Optic Disc Detection From Retinal Images by a Line Operator

Under the framework of computer-aided eye disease diagnosis, this paper presents an automatic optic disc (OD) detection technique. The proposed technique makes use of the unique circular brightness structure associated with the OD, i.e., the OD usually has a circular shape and is brighter than the surrounding pixels whose intensity becomes darker gradually with their distances from the OD center. A line operator is designed to capture such circular brightness structure, which evaluates the image brightness variation along multiple line segments of specific orientations that pass through each retinal image pixel. The orientation of the line segment with the minimum/maximum variation has specific pattern that can be used to locate the OD accurately. The proposed technique has been tested over four public datasets that include 130, 89, 40, and 81 images of healthy and pathological retinas, respectively. Experiments show that the designed line operator is tolerant to different types of retinal lesion and imaging artifacts, and an average OD detection accuracy of 97.4% is obtained.

Blurred image region detection and classification

Many digital images contain blurred regions which are caused by motion or defocus. Automatic detection and classification of blurred image regions are very important for different multimedia analyzing tasks. This paper presents a simple and effective automatic image blurred region detection and classification technique. In the proposed technique, blurred image regions are first detected by examining singular value information for each image pixels. The blur types (i.e. motion blur or defocus blur) are then determined based on certain alpha channel constraint that requires neither image deblurring nor blur kernel estimation. Extensive experiments have been conducted over a dataset that consists of 200 blurred image regions and 200 image regions with no blur that are extracted from 100 digital images. Experimental results show that the proposed technique detects and classifies the two types of image blurs accurately. The proposed technique can be used in many different multimedia analysis applications such as image segmentation, depth estimation and information retrieval.

Perspective rectification of document images using fuzzy set and morphological operations

In this paper, we deal with the problem of document image rectification from image captured by digital cameras. The improvement on the resolution of digital camera sensors has brought more and more applications for non-contact text capture. Unfortunately, perspective distortion in the resulting image makes it hard to properly identify the contents of the captured text using traditional optical character recognition (OCR) systems. We propose in this work a new technique, which is capable of removing perspective distortion and recovering the fronto-parallel view of text with a single image. Different from reported approaches in the literature, the image rectification is carried out using character stroke boundaries and tip points (SBTP), which are extracted from character strokes based on multiple fuzzy sets and morphological operators. The algorithm needs neither high-contrast document boundary (HDB) nor paragraph formatting (PF) information. Experimental results show that our rectification process is fast and robust.