A Technical Review on Comparison and Estimation of Steganographic Tools

Steganography is technique of hiding a data under cover media using different steganography tools. Image steganography is hiding of data (Text/Image/Audio/Video) under a cover as Image. This review paper presents classification of image steganography and the comparison of various Image steganography tools using different image formats. Analyzing numerous tools on the basis of Image features and extracting the best one. Some of the tools available in the market were selected based on the frequent use; these tools were tested using the same input on all of them. Specific text was embedded within all host images for each of the six Steganography tools selected. The results of the experiment reveal that all the six tools were relatively performing at the same level, though some software performs better than others through efficiency. And it was based on the image features like size, dimensions, and pixel value and histogram differentiation.

Design and Development of a Framework For Stroke-Based Handwritten Gujarati Font Generation

Handwritten font generation is important for preserving cultural heritage and creating personalized designs. It adds an authentic and expressive touch to printed materials, making them visually appealing and establishing a stronger connection with the audience. This paper aims to design a framework for generating handwritten fonts in the Gujarati script, mimicking the variation of human handwriting. The proposed font generation model consists of a learning phase and a generation phase. In the learning phase, Gujarati scripts are analyzed, and rules for designing each character are formulated. This ruleset involves the concatenation of strokes in a stroke-based manner, ensuring visual consistency in the resulting glyphs. The generation phase involves the user providing a small subset of characters, and the system automatically generates the remaining character glyphs based on extracted strokes and learned rules, resulting in handwritten Gujarati fonts. The resulting character glyphs are converted into an open-type font using the FontForge tool, making them compatible with any Gujarati editor. Both subjective and objective evaluations are conducted to assess the synthesized images and fonts. Subjective evaluation through user studies provides feedback on quality and visual appeal, achieving an overall accuracy of 84.84%. Notably, eleven characters demonstrated a success ratio above 90%. Objective evaluation using an existing recognition system achieves an overall accuracy of 84.28% in OCR evaluation. Notably, fifteen characters had a success ratio of 80% or higher.