A Robust Algorithm for Text String Separation from Mixed Text/Graphics Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
System for an intelligent office document analysis, recognition, and description
Signal Processing - Intelligent systems for signal and image understanding
Text segmentation using Gabor filters for automatic document processing
Machine Vision and Applications - Special issue: document image analysis techniques
Page segmentation using the description of the background
Computer Vision and Image Understanding - Special issue on document image understanding and retrieval
ACM '86 Proceedings of 1986 ACM Fall joint computer conference
Multiscale Segmentation of Unstructured Document Pages Using Soft Decision Integration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text/Graphics Separation Using Agent-Based Pyramid Operations
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Design of efficient M-band coders with linear-phase andperfect-reconstruction properties
IEEE Transactions on Signal Processing
Locating Text in Images using Matched Wavelets
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
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In this work we propose an algorithm for segmentation of the text and non-text parts of document image using multiscale feature vectors. We assume that the text and non-text parts have different textural properties. M-band wavelets are used as the feature extractors and the features give measures of local energies at different scales and orientations around each pixel of the M×M bandpass channel outputs. The resulting multiscale feature vectors are classified by an unsupervised clustering algorithm to achieve the required segmentation, assuming no a priori information regarding the font size, scanning resolution, type layout etc. of the document.