Recognition and Detection of Language on Inscriptions

Ancient language Font Recognition is one of the Challenging tasks in Optical Character Recognition and Document Analysis. Most of the existing methods are for font recognition make use of local typographical features and connected component analysis. In this paper, Ancient language font recognition is done based on global texture analysis. Ancient language characters are different from currentnbsp centuryrsquos Ancient language character. This paper concentrates on the century identification of ancient language characters and converting them into current centuryrsquos form using MATLAB. Recognition of ancient language hand written characters from inscriptions is difficult. In this paper, a method for recognizing Ancient language characters from stone inscriptions, called the contour-let transform, which has been recently introduced, is adopted. From the previous research works, itrsquos noticed that Wavelet transforms are not capable of reconstructing curved images are perfectly. The contour-let transform offers a solution to remedy to this insufficiency. Contour-let transform is a 3D approach technique where as wavelet transform is a 2D technique. The characters from the input image are recognized through the clustering mechanism. Further the noise is present in the image is removed by fuzzy median filters. Neural networks are been employed to train the image and compare the data with the current centuryrsquos character. hence a more accurate recognition of Ancient language characters from stone inscriptions is obtained

Multi-Content GAN for Few-Shot Font Style Transfer

In this work, we focus on the challenge of taking partial observations of highly-stylized text and generalizing the observations to generate unobserved glyphs in the ornamented typeface. To generate a set of multi-content images following a consistent style from very few examples, we propose an end-to-end stacked conditional GAN model considering content along channels and style along network layers. Our proposed network transfers the style of given glyphs to the contents of unseen ones, capturing highly stylized fonts found in the real-world such as those on movie posters or infographics. We seek to transfer both the typographic stylization (ex. serifs and ears) as well as the textual stylization (ex. color gradients and effects.) We base our experiments on our collected data set including 10,000 fonts with different styles and demonstrate effective generalization from a very small number of observed glyphs

Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields

This work presents a first evaluation of using spatio-temporal receptive fields from a recently proposed time-causal spatio-temporal scale-space framework as primitives for video analysis. We propose a new family of video descriptors based on regional statistics of spatio-temporal receptive field responses and evaluate this approach on the problem of dynamic texture recognition. Our approach generalises a previously used method, based on joint histograms of receptive field responses, from the spatial to the spatio-temporal domain and from object recognition to dynamic texture recognition. The time-recursive formulation enables computationally efficient time-causal recognition. The experimental evaluation demonstrates competitive performance compared to state-of-the-art. Especially, it is shown that binary versions of our dynamic texture descriptors achieve improved performance compared to a large range of similar methods using different primitives either handcrafted or learned from data. Further, our qualitative and quantitative investigation into parameter choices and the use of different sets of receptive fields highlights the robustness and flexibility of our approach. Together, these results support the descriptive power of this family of time-causal spatio-temporal receptive fields, validate our approach for dynamic texture recognition and point towards the possibility of designing a range of video analysis methods based on these new time-causal spatio-temporal primitives.Comment: 29 pages, 16 figure