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In recent years, text recognition has achieved remarkable success inrecognizing scanned document text. However, word recognition in natural imagesis still an open problem, which generally requires time consumingpost-processing steps. We present a novel architecture for individual worddetection in scene images based on semantic segmentation. Our contributions aretwofold: the concept of WordFence, which detects border areas surrounding eachindividual word and a novel pixelwise weighted softmax loss function whichpenalizes background and emphasizes small text regions. WordFence ensures thateach word is detected individually, and the new loss function provides a strongtraining signal to both text and word border localization. The proposedtechnique avoids intensive post-processing, producing an end-to-end worddetection system. We achieve superior localization recall on common benchmarkdatasets - 92% recall on ICDAR11 and ICDAR13 and 63% recall on SVT.Furthermore, our end-to-end word recognition system achieves state-of-the-art86% F-Score on ICDAR13.

WordFence: Text Detection in Natural Images with Border Awareness