Print-Scan Resilient Text Image Watermarking Based on Stroke Direction Modulation for Chinese Document Authentication

Print-scan resilient watermarking has emerged as an attractive way for document security. This paper proposes an stroke direction modulation technique for watermarking in Chinese text images. The watermark produced by the idea offers robustness to print-photocopy-scan, yet provides relatively high embedding capacity without losing the transparency. During the embedding phase, the angle of rotatable strokes are quantized to embed the bits. This requires several stages of preprocessing, including stroke generation, junction searching, rotatable stroke decision and character partition. Moreover, shuffling is applied to equalize the uneven embedding capacity. For the data detection, denoising and deskewing mechanisms are used to compensate for the distortions induced by hardcopy. Experimental results show that our technique attains high detection accuracy against distortions resulting from print-scan operations, good quality photocopies and benign attacks in accord with the future goal of soft authentication

Text Line Segmentation of Historical Documents: a Survey

There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines),automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade, and dedicated to documents of historical interest.Comment: 25 pages, submitted version, To appear in International Journal on Document Analysis and Recognition, On line version available at http://www.springerlink.com/content/k2813176280456k3

Cascaded 3D Full-body Pose Regression from Single Depth Image at 100 FPS

There are increasing real-time live applications in virtual reality, where it plays an important role in capturing and retargetting 3D human pose. But it is still challenging to estimate accurate 3D pose from consumer imaging devices such as depth camera. This paper presents a novel cascaded 3D full-body pose regression method to estimate accurate pose from a single depth image at 100 fps. The key idea is to train cascaded regressors based on Gradient Boosting algorithm from pre-recorded human motion capture database. By incorporating hierarchical kinematics model of human pose into the learning procedure, we can directly estimate accurate 3D joint angles instead of joint positions. The biggest advantage of this model is that the bone length can be preserved during the whole 3D pose estimation procedure, which leads to more effective features and higher pose estimation accuracy. Our method can be used as an initialization procedure when combining with tracking methods. We demonstrate the power of our method on a wide range of synthesized human motion data from CMU mocap database, Human3.6M dataset and real human movements data captured in real time. In our comparison against previous 3D pose estimation methods and commercial system such as Kinect 2017, we achieve the state-of-the-art accuracy

Arabic Typed Text Recognition in Graphics Images (ATTR-GI)

While optical character recognition (OCR) techniques may perform well on standard text documents, their performance degrades significantly in graphics images. In standard scanned text documents OCR techniques enjoy a number of convenient assumptions such as clear backgrounds, standard fonts, predefined line orientation, page size, the start point of written. These assumptions are not true in graphics documents such as Arabic advertisements, personal cards, screenshot. Therefore, in such types of images, greater attention is required in the initial stage of detecting Arabic text regions in order for subsequent character recognition steps to be successful. Special features of Arabic alphabet characters introduce additional challenges which are not present in Latin alphabet characters. In this research we propose a new technique for automatically detecting text in graphics documents, and preparing them for OCR processing. Our detection approach is based on some mathematical measurements to know is it a text or not and to know is it Arabic Based Text or Latin Based. These measurements are follows, measure the Base Line (the line has maximum number of black pixels). Also, measure Item Area (the content of extracted sub images). Finally, find maximum peak for the adjacent black pixels in Base line and maximum length for sub adjacent black pixels. Our experiment results will come in more details. We believe our technique will enable OCR systems to overcome their major shortcoming when dealing with text in graphics images. This will further enable a variety of OCR-based applications to extend their operation to graphics documents such as SPAM detection from image, reading advertisement for blind people, search and index document which contain image, enhancing for printer property (black white or color printer) and enhancing OCR

Real-time Online Chinese Character Recognition

In this project, I built a web application for handwritten Chinese characters recognition in real time. This system determines a Chinese character while a user is drawing/writing it. The techniques and steps I use to build the recognition system include data preparation, preprocessing, features extraction, and classification. To increase the accuracy, two different types of neural networks ared used in the system: a multi-layer neural network and a convolutional neural network

A Robot Calligraphy System: From Simple to Complex Writing by Human Gestures

Robotic writing is a very challenging task and involves complicated kinematic control algorithms and image processing work. This paper, alternatively, proposes a robot calligraphy system that firstly applies human arm gestures to establish a font database of Chinese character elementary strokes and English letters, then uses the created database and human gestures to write Chinese characters and English words. A three-dimensional motion sensing input device is deployed to capture the human arm trajectories, which are used to build the font database and to train a classifier ensemble. 26 types of human gesture are used for writing English letters, and 5 types of gesture are used to generate 5 elementary strokes for writing Chinese characters. By using the font database, the robot calligraphy system acquires a basic writing ability to write simple strokes and letters. Then, the robot can develop to write complex Chinese characters and English words by following human body movements. The classifier ensemble, which is used to identify each gesture, is implemented through using feature selection techniques and the harmony search algorithm, thereby achieving better classification performance. The experimental evaluations are carried out to demonstrate the feasibility and performance of the proposed method. By following the motion trajectories of the human right arm, the end-effector of the robot can successfully write the English words or Chinese characters that correspond to the arm trajectories

Recognition of Arabic handwritten words

Recognizing Arabic handwritten words is a difficult problem due to the deformations of different writing styles. Moreover, the cursive nature of the Arabic writing makes correct segmentation of characters an almost impossible task. While there are many sub systems in an Arabic words recognition system, in this work we develop a sub system to recognize Part of Arabic Words (PAW). We try to solve this problem using three different approaches, implicit segmentation and two variants of holistic approach. While Rothacker found similar conclusions while this work is being prepared, we report the difficulty in locating characters in PAW using Scale Invariant Feature Transforms under the first approach. In the second and third approaches, we use holistic approach to recognize PAW using Support Vector Machine (SVM) and Active Shape Models (ASM). While there are few works that use SVM to recognize PAW, they use a small dataset; we use a large dataset and a different set of features. We also explain the errors SVM and ASM make and propose some remedies to these errors as future work

Feature Extraction Methods for Character Recognition

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