N-FTRN: Neighborhoods based fully convolutional network for Chinese text line recognition

Abstract

The convolutional recurrent neural network is one of the most popular text recognition methods. Recurrent structures can extract long-term dependencies, but they are time consuming in computation compared with convolutional structures. We argue that the Chinese text line recognition can be performed based on neighbor rather than entire contextual information, and the information extracted from neighborhoods should only be a supplement to the information extracted from character regions. Therefore, we propose a novel neighborhoods based fully convolutional text recognition network (N-FTRN). It first extracts character-level feature sequences from text lines, then uses residual blocks instead of the recurrent structure to utilize contextual information. A reshape layer is applied to enable the network to recognize both vertical and horizontal text lines. Extensive experiments have been conducted to validate the efficiency and effectiveness of the proposed network. Compared with the state-of-the-art methods, we achieve comparable recognition performances on a Chinese scene text competition dataset (TRW) in ICDAR 2015 with much more compact models.