# FACLSTM: ConvLSTM with Focused Attention for Scene Text Recognition

## TL;DR

This paper introduces FACLSTM, a ConvLSTM-based scene text recognition model that leverages spatial correlations and focused attention to improve recognition accuracy, especially on curved and noisy text images.

## Contribution

The paper proposes a novel ConvLSTM architecture with integrated focused attention and character masks for improved scene text recognition.

## Key findings

- Outperforms state-of-the-art on curved text datasets
- Effective on low-resolution and noisy images
- Competitive results on standard benchmarks

## Abstract

Scene text recognition has recently been widely treated as a sequence-to-sequence prediction problem, where traditional fully-connected-LSTM (FC-LSTM) has played a critical role. Due to the limitation of FC-LSTM, existing methods have to convert 2-D feature maps into 1-D sequential feature vectors, resulting in severe damages of the valuable spatial and structural information of text images. In this paper, we argue that scene text recognition is essentially a spatiotemporal prediction problem for its 2-D image inputs, and propose a convolution LSTM (ConvLSTM)-based scene text recognizer, namely, FACLSTM, i.e., Focused Attention ConvLSTM, where the spatial correlation of pixels is fully leveraged when performing sequential prediction with LSTM. Particularly, the attention mechanism is properly incorporated into an efficient ConvLSTM structure via the convolutional operations and additional character center masks are generated to help focus attention on right feature areas. The experimental results on benchmark datasets IIIT5K, SVT and CUTE demonstrate that our proposed FACLSTM performs competitively on the regular, low-resolution and noisy text images, and outperforms the state-of-the-art approaches on the curved text with large margins.