Enhanced Attention-Based Encoder-Decoder Framework for Text Recognition

Recognizing irregular text in natural images is a challenging task in computer vision. The existing approaches still face difficulties in recognizing irregular text because of its diverse shapes. In this paper, we propose a simple yet powerful irregular text recognition framework based on an encoder-decoder architecture. The proposed framework is divided into four main modules. Firstly, in the image transformation module, a Thin Plate Spline (TPS) transformation is employed to transform the irregular text image into a readable text image. Secondly, we propose a novel Spatial Attention Module (SAM) to compel the model to concentrate on text regions and obtain enriched feature maps. Thirdly, a deep bi-directional long short-term memory (Bi-LSTM) network is used to make a contextual feature map out of a visual feature map generated from a Convolutional Neural Network (CNN). Finally, we propose a Dual Step Attention Mechanism (DSAM) integrated with the Connectionist Temporal Classification (CTC) - Attention decoder to re-weights visual features and focus on the intra-sequence relationships to generate a more accurate character sequence. The effectiveness of our proposed framework is verified through extensive experiments on various benchmarks datasets, such as SVT, ICDAR, CUTE80, and IIIT5k. The performance of the proposed text recognition framework is analyzed with the accuracy metric. Demonstrate that our proposed method outperforms the existing approaches on both regular and irregular text. Additionally, the robustness of our approach is evaluated using the grocery datasets, such as GroZi-120, WebMarket, SKU-110K, and Freiburg Groceries datasets that contain complex text images. Still, our framework produces superior performance on grocery datasets.