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

Word-wise South Indian Script Identification using GLCM and Radon Features

This paper presents a hybrid features for identification of south Indian scripts in word-wise and it has used three classifiers. We have used two kinds of features namely Radon and Gray Level Co-occurrence Matrix (GLCM) and combination of Radon and GLCM features. For identification purpose LDA, KNN and SVM classifiers have been employed. For the experiment proposed work considered the 6 languages scripts; Roman, Devnagari, Kannada, Telugu, Tamil and Malayalam. This proposed work considered the Word Image Dataset for 11 Languages form MILE Lab IISC in this dataset proposed work considered 6 languages with 5000 for each scripts, this makes total of 30,000 word images. We have made the total of five bi-lingual combinations of south Indian scripts. To extract features; GLCM and Radon Features are considered (4 features of GLCM, 11 features, for Radon we obtained 98.80% from KNN for the Roman and Kannada combination, for GLCM 88.20% obtained by SVM for the Roman and Kannada from SVM Classifier and from combination of Radon and GLCM we have obtained the accuracy of 98.90% for Roman and Kannada combination scripts

Use of colour for hand-filled form analysis and recognition

Colour information in form analysis is currently under utilised. As technology has advanced and computing costs have reduced, the processing of forms in colour has now become practicable. This paper describes a novel colour-based approach to the extraction of filled data from colour form images. Images are first quantised to reduce the colour complexity and data is extracted by examining the colour characteristics of the images. The improved performance of the proposed method has been verified by comparing the processing time, recognition rate, extraction precision and recall rate to that of an equivalent black and white system