scholarly journals Tamper Detection in Text Document

2008 ◽  
Vol 5 (2) ◽  
pp. 313-317
Author(s):  
Baghdad Science Journal
Keyword(s):  
Center Of Gravity ◽  
Tamper Detection ◽  
New Method ◽  
Document Images ◽  
Middle Point ◽  
Clear Separation ◽  
Binary Nature ◽  
Letter Word ◽  
Text Document ◽  
Object Based

Although text document images authentication is difficult due to the binary nature and clear separation between the background and foreground but it is getting higher demand for many applications. Most previous researches in this field depend on insertion watermark in the document, the drawback in these techniques lie in the fact that changing pixel values in a binary document could introduce irregularities that are very visually noticeable. In this paper, a new method is proposed for object-based text document authentication, in which I propose a different approach where a text document is signed by shifting individual words slightly left or right from their original positions to make the center of gravity for each line fall in with the middle point of intended line. Any modification, addition or deletion in a letter, word, or line in the document will be detected.

A New Method for Detecting Altered Text in Document Images

2021 ◽  
pp. 2160010
Author(s):  
Lokesh Nandanwar ◽  
Palaiahnakote Shivakumara ◽  
Umapada Pal ◽  
Tong Lu ◽  
Daniel Lopresti ◽  
...  
Keyword(s):  
Spatial Information ◽  
Feature Vector ◽  
New Method ◽  
Document Images ◽  
Positive And Negative Coefficients ◽  
Digital Format ◽  
Fused Image ◽  
Fusion Operation ◽  
The Relationship ◽  
Text Images

As more and more office documents are captured, stored, and shared in digital format, and as image editing software are becoming increasingly more powerful, there is a growing concern about document authenticity. To prevent illicit activities, this paper presents a new method for detecting altered text in document images. The proposed method explores the relationship between positive and negative coefficients of DCT to extract the effect of distortions caused by tampering by fusing reconstructed images of respective positive and negative coefficients, which results in Positive-Negative DCT coefficients Fusion (PNDF). To take advantage of spatial information, we propose to fuse R, G, and B color channels of input images, which results in RGBF (RGB Fusion). Next, the same fusion operation is used for fusing PNDF and RGBF, which results in a fused image for the original input one. We compute a histogram to extract features from the fused image, which results in a feature vector. The feature vector is then fed to a deep neural network for classifying altered text images. The proposed method is tested on our own dataset and the standard datasets from the ICPR 2018 Fraud Contest, Altered Handwriting (AH), and faked IMEI number images. The results show that the proposed method is effective and the proposed method outperforms the existing methods irrespective of image type.

Automatic Anonymization of Printed-Text Document Images

2018 ◽  
pp. 145-152
Author(s):  
Ángel Sánchez ◽  
José F. Vélez ◽  
Javier Sánchez ◽  
A. Belén Moreno
Keyword(s):  
Document Images ◽  
Text Document ◽  
Printed Text

Extended Geometric Filter for Reconstruction as a Basis for Computational Inspection

2008 ◽  
Author(s):  
Alexander Miropolsky ◽  
Anath Fischer
Keyword(s):  
Quality Control ◽  
Computer Model ◽  
Large Scale ◽  
Manufacturing Industry ◽  
New Method ◽  
Reconstruction Methods ◽  
Object Based ◽  
Inspection Process ◽  
Important Quality ◽  
Scan Data

Inspection of machined objects is one of the most important quality control tasks in the manufacturing industry. Contemporary scanning technologies have provided the impetus for the development of computational inspection methods, where the computer model of the manufactured object is reconstructed from the scan data, and then verified against its design computer model. Scan data, however, is typically very large scale (i.e. many points), unorganized, noisy and incomplete. Therefore, reconstruction is problematic. To overcome the above problems the reconstruction methods may exploit diverse feature data, that is, diverse information about the properties of the scanned object. Based on this concept, the paper proposes a new method for de-noising and reduction of scan data by Extended Geometric Filter (EGF). The proposed method is applied directly on the scanned points and is automatic, fast and straightforward to implement. The paper demonstrates the integration of the proposed method into the framework of the computational inspection process.

scholarly journals A new method of vegetation mapping by object-based classification using high resolution satellite data

2006 ◽  
Vol 45 (1) ◽  
pp. 43-49 ◽  
Author(s):  
Noritoshi KAMAGATA ◽  
Keitarou HARA ◽  
Masaru MORI ◽  
Yukio AKAMATSU ◽  
Yunqing LI ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Vegetation Mapping ◽  
New Method ◽  
Object Based

scholarly journals Corpus-based technique for improving Arabic OCR system

2021 ◽  
Vol 21 (1) ◽  
pp. 233
Author(s):  
Ahmed Hussain Aliwy ◽  
Basheer Al-Sadawi
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Language Model ◽  
Arabic Language ◽  
Document Images ◽  
Statistical Language Model ◽  
Text Document ◽  
Optical Character ◽  
Arabic Ocr

<p><span>An optical character recognition (OCR) refers to a process of converting the text document images into editable and searchable text. OCR process poses several challenges in particular in the Arabic language due to it has caused a high percentage of errors. In this paper, a method, to improve the outputs of the Arabic Optical character recognition (AOCR) Systems is suggested based on a statistical language model built from the available huge corpora. This method includes detecting and correcting non-word and real words error according to the context of the word in the sentence. The results show that the percentage of improvement in the results is up to (98%) as a new accuracy for AOCR output. </span></p>

Watermarking text document images using edge direction histograms

2004 ◽  
Vol 25 (11) ◽  
pp. 1243-1251 ◽  
Author(s):  
Young-Won Kim ◽  
Il-Seok Oh
Keyword(s):  
Document Images ◽  
Edge Direction ◽  
Text Document
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