Automatic separation of signatures from a document page involves difficult challenges due to the free-flow nature of handwriting, overlapping/touching of signature parts with printed text, noise, etc. In this paper, w...
详细信息
ISBN:
(纸本)9781457713507
Automatic separation of signatures from a document page involves difficult challenges due to the free-flow nature of handwriting, overlapping/touching of signature parts with printed text, noise, etc. In this paper, we have proposed a novel approach for the segmentation of signatures from machine printed signed documents. The algorithm first locates the signature block in the document using word level feature extraction. Next, the signature strokes that touch or overlap with the printed texts are separated. A stroke level classification is then performed using skeleton analysis to separate the overlapping strokes of printed text from the signature. Gradient based features and Support Vector Machine (SVM) are used in our scheme. Finally, a Conditional Random Field (CRF) model energy minimization concept based on approximated labeling by graph cut is applied to label the strokes as "signature" or "printed text" for accurate segmentation of signatures. Signature segmentation experiment is performed in "tobacco" dataset1 and we have obtained encouraging results.
This paper presents a survey on sclera-based biometric recognition. Among the various biometric methods, sclera is one of the novel and promising biometric techniques. The sclera, a white region of connective tissue a...
详细信息
This paper presents a survey on sclera-based biometric recognition. Among the various biometric methods, sclera is one of the novel and promising biometric techniques. The sclera, a white region of connective tissue and blood vessels, surrounds the iris. A survey of the techniques available in the area of sclera biometrics will be of great assistance to researchers, and hence a comprehensive effort is made in this article to discuss the advancements reported in this regard during the past few decades. As a limited number of publications are found in the literature, an attempt is made in this paper to increase awareness of this area so that the topic gains popularity and interest among researchers. In this survey, a brief introduction is given initially about the sclera biometric, which is subsequently followed by background concepts, various pre-processing techniques, feature extraction and finally classification techniques associated with the sclera biometric. Benchmarking databases are very important for any patternrecognition related research, so the databases related with this work is also discussed. Finally, our observations, future scope and existing difficulties, which are unsolved in sclera biometrics, are discussed. We hope that this survey will serve to focus more researcher attention towards the emerging sclera biometric.
There are many video images where hand written text may appear. Therefore handwritten scene text detection in video is essential and useful for many applications for efficient indexing, retrieval etc. Also there are m...
详细信息
There are many video images where hand written text may appear. Therefore handwritten scene text detection in video is essential and useful for many applications for efficient indexing, retrieval etc. Also there are many video frames where text line may be multi-oriented in nature. To the best of our knowledge there is no work on handwritten text detection in video, which is multi-oriented in nature. In this paper, we present a new method based on maximum color difference and boundary growing method for detection of multi-oriented handwritten scene text in video. The method computes maximum color difference for the average of R, G and B channels of the original frame to enhance the text information. The output of maximum color difference is fed to a K-means algorithm with K = 2 to separate text and non-text clusters. Text candidates are obtained by intersecting the text cluster with the Sobel output of the original frame. To tackle the fundamental problem of different orientations and skews of handwritten text, boundary growing method based on a nearest neighbor concept is employed. We evaluate the proposed method by testing on our own handwritten text database and publicly available video data (Hua's data). Experimental results obtained from the proposed method are promising.
Questioned Document Examination processes often encompass analysis of torn documents. To aid a forensic expert, automatic classification of content type in torn documents might be useful. This helps a forensic expert ...
详细信息
Questioned Document Examination processes often encompass analysis of torn documents. To aid a forensic expert, automatic classification of content type in torn documents might be useful. This helps a forensic expert to sort out similar document fragments from a pile of torn documents. One parameter of similarity could be the script of the text. In this article we propose a method to identify the script in document fragments. Torn documents are normally characterized by text with arbitrary orientation. We use Zernike moment - based feature that is rotation invariant together with Support Vector Machine (SVM) to classify the script type. Subsequently gradient features are used for comparative analysis of results between rotation dependent and rotation invariant feature type. We achieved an overall script-identification accuracy of 81.39% when dealing with 11 different scripts at character/connected-component level and 94.65% at word level.
Although there are advanced technologies for character recognition, automatic descriptive answer evaluation is an open challenge for the document image analysis community due to large diversified handwritten text and ...
详细信息
Optical Character recognition (OCR) in video stream of flipping pages is a challenging task because flipping at random speed cause difficulties to identify frames that contain the open page image (OPI) for better read...
详细信息
Optical Character recognition (OCR) in video stream of flipping pages is a challenging task because flipping at random speed cause difficulties to identify frames that contain the open page image (OPI) for better readability. Also, low resolution, blurring effect shadows add significant noise in selection of proper frames for OCR. In this work, we focus on the problem of identifying the set of optimal representative frames for the OPI from a video stream of flipping pages and then perform OCR without using any explicit hardware. To the best of our knowledge this is the first work in this area. We present an algorithm that exploits cues from edge information of flipping pages. These cues, extracted from the region of interest (ROI) of the frame, determine the flipping or open state of a page. Then a SVM classifier is trained with the edge cue information for this determination. For each OPI we obtain a set of frames. Next we choose the central frame from that set of frames as the representative frame of the corresponding OPI and perform OCR. Experiments are performed on video documents recorded using a standard resolution camera to validate the frame selection algorithm and we have obtained 88% accuracy. Also, we have obtained character recognition accuracy of 82% and word recognition accuracy of 77% from such book flipping OCR.
Methods developed for normal 2D text detection do not work well for text that is rendered using decorative, 3D effects, etc. This paper proposes a new method for classification of 2D and 3D natural scene text images s...
详细信息
In this paper, a new sequence-matching algorithm, called as Flexible Sequence Matching (FSM) algorithm is proposed. FSM combines several abilities of other sequence matching algorithms (especially DTW, CDP and MVM) th...
详细信息
In this paper, a new sequence-matching algorithm, called as Flexible Sequence Matching (FSM) algorithm is proposed. FSM combines several abilities of other sequence matching algorithms (especially DTW, CDP and MVM) that could be configured depending on the application domain. Its generality and robustness comes from its ability to find sub sequences (as in CDP), to skip outliers inside the match sequences (as in MVM) and to match multiple elements with a single one (as in CDP and DTW). These properties make it extremely suitable for robust word spotting. More precisely, the FSM algorithm has the capability to retrieve a query inside a line or piece of line. This facility is useful as word segmentation process may not work accurately or when only line segmentation information is available. Furthermore, thanks to its skipping capability, that makes the proposed FSM algorithm less sensible to local variations in the spelling of words, and also to local degradation effects. Finally, its multiple matching facilities (many to one and one to many matching) are useful in case of different length of target and query sequences due to the variability in scale factor. We demonstrate the superiority of proposed FSM algorithm in specific cases such as incorrect word segmentation and word level local variations. When different experiments were performed using handwritten George Washington dataset and also on historical typewritten document images, quite promising results were obtained.
In this paper, a text line identification method is proposed. The text lines of printed document are easy to segment due to uniform straightness of the lines and sufficient gap between the lines. But in handwritten do...
详细信息
In this paper, a text line identification method is proposed. The text lines of printed document are easy to segment due to uniform straightness of the lines and sufficient gap between the lines. But in handwritten documents, the line is nonuniform and interline gaps are variable. We take Rabindranath Tagore's manuscript as it is one of the most difficult manuscripts that contain doodles. Our method consists of a preprocessing stage to clean the document image. Then we separate doodles from the manuscript to get the textual region. After that we identify the text lines on the manuscript. For text line identification, we use window examination, black run-length smearing, horizontal histogram and connected component analysis.
暂无评论