Accurate Scene Text Recognition Based on Recurrent Neural Network

• Published in: Computer Vision -- ACCV 2014 Vol. 9003; pp. 35 - 48
• Main Authors: Su, Bolan, Lu, Shijian
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2015; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Artificial intelligence; Bidirectional LSTM; Column Feature Vector; Image processing; Long Short-term Memory (LSTM); Pattern recognition; Scene Text Recognition; Word Image
• ISBN: 3319168649; 9783319168647
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-16865-4_3

Scene text recognition is a useful but very challenging task due to uncontrolled condition of text in natural scenes. This paper presents a novel approach to recognize text in scene images. In the proposed technique, a word image is first converted into a sequential column vectors based on Histogram of Oriented Gradient (HOG). The Recurrent Neural Network (RNN) is then adapted to classify the sequential feature vectors into the corresponding word. Compared with most of the existing methods that follow a bottom-up approach to form words by grouping the recognized characters, our proposed method is able to recognize the whole word images without character-level segmentation and recognition. Experiments on a number of publicly available datasets show that the proposed method outperforms the state-of-the-art techniques significantly. In addition, the recognition results on publicly available datasets provide a good benchmark for the future research in this area.