A Font Search Engine for Large Font Databases

A search engine for font recognition is presented and evaluated. The intended usage is the search in very large font databases. The input to the search engine is an image of a text line, and the output is the name of the font used when rendering the text. After pre-processing and segmentation of the input image, a local approach is used, where features are calculated for individual characters. The method is based on eigenimages calculated from edge filtered character images, which enables compact feature vectors that can be computed rapidly. In this study the database contains 2763 different fonts for the English alphabet. To resemble a real life situation, the proposed method is evaluated with printed and scanned text lines and character images. Our evaluation shows that for 99.1% of the queries, the correct font name can be found within the five best matches

Neural Network Approach for Character Recognition and Text Detection: A Survey

ABSTRACT Text detection and Character recognition from the image has been one of the most interesting and challenging research areas in field of pattern recognition, artificial intelligence, machine vision and image processing in the recent years. There are basically four steps that include preprocessing, feature extraction, candidate's selection, and desired character recognition to develop any of the character recognition system. Optical character recognition is the technique to convert text from the image into computer or machine readable form. Like intelligent character recognition (ICR) one character is taken at a time to make it editable by the machine. There are several approaches for developing the OCR system but in this review we emphasis on OCR using artificial neural network trained by back propagation algorithm and fuzzy logic

Database-driven Mathematical Character Recognition

Abstract. We present an approach for recognising mathematical texts using an extensive L ATEX symbol database and a novel recognition algorithm. The process consists essentially of three steps: Recognising the individual characters in a mathematical text by relating them to glyphs in the database of symbols, analysing the recognised glyphs to determine the closest corresponding L ATEX symbol, and reassembling the text by putting the appropriate L ATEX commands at their corresponding positions of the original text inside a L ATEX picture environment. The recogniser itself is based on a novel variation on the application of geometric moment invariants. The working system is implemented in Java.