SCATTER: Selective Context Attentional Scene Text Recognizer

• URL: http://arxiv.org/abs/2003.11288v1
• Date: Wed, 25 Mar 2020 09:20:28 GMT
• Title: SCATTER: Selective Context Attentional Scene Text Recognizer
• Authors: Ron Litman, Oron Anschel, Shahar Tsiper, Roee Litman, Shai Mazor and R. Manmatha
• Abstract summary: Scene Text Recognition (STR) is the task of recognizing text against complex image backgrounds. Current state-of-the-art (SOTA) methods still struggle to recognize text written in arbitrary shapes. We introduce a novel architecture for STR, named Selective Context ATtentional Text Recognizer (SCATTER)
• Score: 16.311256552979835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scene Text Recognition (STR), the task of recognizing text against complex image backgrounds, is an active area of research. Current state-of-the-art (SOTA) methods still struggle to recognize text written in arbitrary shapes. In this paper, we introduce a novel architecture for STR, named Selective Context ATtentional Text Recognizer (SCATTER). SCATTER utilizes a stacked block architecture with intermediate supervision during training, that paves the way to successfully train a deep BiLSTM encoder, thus improving the encoding of contextual dependencies. Decoding is done using a two-step 1D attention mechanism. The first attention step re-weights visual features from a CNN backbone together with contextual features computed by a BiLSTM layer. The second attention step, similar to previous papers, treats the features as a sequence and attends to the intra-sequence relationships. Experiments show that the proposed approach surpasses SOTA performance on irregular text recognition benchmarks by 3.7\% on average.

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