Unsupervised Text Extraction from G-Maps

This paper represents an text extraction method from Google maps, GIS maps/images. Due to an unsupervised approach there is no requirement of any prior knowledge or training set about the textual and non-textual parts. Fuzzy CMeans clustering technique is used for image segmentation and Prewitt method is used to detect the edges. Connected component analysis and gridding technique enhance the correctness of the results. The proposed method reaches 98.5% accuracy level on the basis of experimental data sets.Comment: Proc. IEEE Conf. #30853, International Conference on Human Computer Interactions (ICHCI'13), Chennai, India, 23-24 Aug., 201

Visual Eureka Navigating Images Through Textual Queries

Within the domain of text extraction technologies, progress has been somewhat constrained, notwithstanding notable instances such as Google Lens, which proficiently extracts text from images. A conspicuous gap persists, however, in the availability of software tailored for the reciprocal task of searching images based on their textual content. Our pioneering conceptual framework introduces a transformative paradigm shift—a software solution engineered for image retrieval through text search. The crux of our technical innovation lies in the systematic incorporation of metadata as a repository for textual data linked to images. Through advanced text extraction algorithms, including robust optical character recognition methods, we decipher and store relevant textual information in this metadata. This meticulous indexing facilitates a highly efficient search mechanism, allowing users to query images based on specific text-related parameters. The user interface seamlessly integrates these functionalities, providing an intuitive platform for users to input text queries and retrieve images with unprecedented precision. Scalability and performance optimization measures ensure the system's adaptability to growing datasets, promising not only a redefined utility of image search but also a significant advancement in user convenience and operational efficiency within the visual data retrieval landscape

Region-based caption text extraction

This paper presents a method for caption text detection. The proposed method will be included in a generic indexing system dealing with other semantic concepts which are to be automatically detected as well. To have a coherent detection system, the various object detection algorithms use a common image description. In our framework, the image description is a hierarchical region-based image model. The proposed method takes advantage of texture and geometric features to detect the caption text. Texture features are estimated using wavelet analysis and mainly applied for Text candidate spotting. In turn, Text characteristics verification is basically carry out relying on geometric features, which are estimated exploiting the region-based image model. Analysis of the region hierarchy provides the final caption text objects. The final step of Consistency analysis for output is performed by a binarization algorithm that robustly estimates the thresholds on the caption text area of support.Peer ReviewedPostprint (published version

Region-based caption text extraction

This chapter presents a method for caption text detection. The proposed method will be included in a generic indexing system dealing with other semantic concepts which are to be automatically detected as well. To have a coherent detection system, the various object detection algorithms use a common image description, a hierarchical region-based image model. The proposed method takes advantage of texture and geometric features to detect the caption text. Texture features are estimated using wavelet analysis and mainly applied for text candidate spotting. In turn, text characteristics verification relies on geometric features,which are estimated exploiting the region-based image model. Analysis of the region hierarchy provides the final caption text objects. The final step of consistency analysis for output is performed by a binarization algorithm that robustly estimates the thresholds on the caption text area of support.Peer ReviewedPostprint (published version

Caption text extraction for indexing purposes using a hierarchical region-based image model

This paper presents a technique for detecting caption text for index-ing purposes. This technique is to be included in a generic indexing system dealing with other semantic concepts. The various object detection algorithms are required to share a common image descrip-tion which, in our case, is a hierarchical region-based image model. Caption text objects are detected combining texture and geometric features, which are estimated using wavelet analysis and taking ad-vantage of the region-based image model, respectively. Analysis of the region hierarchy provides the final caption text objects. Index Terms — Image segmentation, feature extraction, object recognition, text recognitio

Caption text extraction for indexing purposes using a hierarchical region-based image model

This paper presents a technique for detecting caption text for indexing purposes. This technique is to be included in a generic indexing system dealing with other semantic concepts. The various object detection algorithms are required to share a common image description which, in our case, is a hierarchical region-based image model. Caption text objects are detected combining texture and geometric features, which are estimated using wavelet analysis and taking advantage of the region-based image model, respectively. Analysis of the region hierarchy provides the final caption text objects.Peer Reviewe

System for caption text extraction on a hierarchical region-based image representation

English: This work presents a technique for detecting caption text for indexing purposes. This technique is to be included in a generic indexing system dealing with other semantic concepts. The various object detection algorithms are required to share a common image description which is a hierarchical region-based image model. Caption text objects are detected combining texture and geometric features, which are estimated using wavelet analysis and taking advantage of the region-based image model, respectively. Analysis of the region hierarchy provides the final caption text objects