This paper proposes a new hierarchical MDL-based model for a joint detection and classi?cation of multilingual text lines in im- ages taken by hand-held cameras. The majority of related text detec- tion methods assume alphabet-based writing in a single language, e.g. in Latin. They use simple clustering heuristics speci?c to such texts: prox- imity between letters within one line, larger distance between separate lines, etc. We are interested in a significantly more ambiguous problem where images combine alphabet and logographic characters from multiple languages and typographic rules vary a lot (e.g. English, Korean, and Chinese). Complexity of detecting and classifying text lines in multiple languages calls for a more principled approach based on information- theoretic principles. Our new MDL model includes data costs combining geometric errors with classi?cation likelihoods and a hierarchical sparsity term based on label costs. This energy model can be e?ciently minimized by fusion moves. We demonstrate robustness of the proposed algorithm on a large new database of multilingual text images collected in the pub- lic transit system of Seoul.
After text candidates are detected non-text blobs must be filtered out. The decision whether a blob represents text is done by classification. Popular classifiers are: support vector machine (SVM) @cite_2 @cite_9 @cite_14 , AdaBoost @cite_7 @cite_32 or their cascades. Popular features for classification are: color based (histogram of intensities, moment of intensity) edge based (histogram of oriented gradients (HOG), Gabor filter) geometric features (width, height, aspect ration, number of holes, convex hull, area of background foreground).
@cite_14 @cite_7 @cite_9 @cite_32 @cite_2
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Text detection and recognition in real images taken in unconstrained environments, such as street view images, remain surprisingly challenging in Computer Vision. Detecting text regions in natural scenes is an important part of computer vision. We propose a novel text detection algorithm that extracts six different classes features of text, and uses Modest AdaBoost with multi-scale sequential search. Experiments show that our algorithm can detect text regions with a f= 0.70, from the ICDAR 2003 datasets which include images with text of various fonts, sizes, colors, alphabets and scripts. Recognition of text in natural scene images is becoming a prominent research area due to the widespread availablity of imaging devices in low-cost consumer products like mobile phones. To evaluate the performance of recent algorithms in detecting and recognizing text from complex images, the ICDAR 2011 Robust Reading Competition was organized. Challenge 2 of the competition dealt specifically with detecting recognizing text in natural scene images. This paper presents an overview of the approaches that the participants used, the evaluation measure, and the dataset used in the Challenge 2 of the contest. We also report the performance of all participating methods for text localization and word recognition tasks and compare their results using standard methods of area precision recall and edit distance. We present a fast automatic text detection algorithm devised for a mobile augmented reality (AR) translation system on a mobile phone. In this application, scene text must be detected, recognized, and translated into a desired language, and then the translation is displayed overlaid properly on the real-world scene. In order to offer a fast automatic text detector, we focused our initial search to find a single letter. Detecting one letter provides useful information that is processed with efficient rules to quickly find the reminder of a word. This approach allows for detecting all the contiguous text regions in an image quickly. We also present a method that exploits the redundancy of the information contained in the video stream to remove false alarms. Our experimental results quantify the accuracy and efficiency of the algorithm and show the strengths and weaknesses of the method as well as its speed (about 160 ms on a recent generation smartphone, not optimized). The algorithm is well suited for real-time, real-world applications.