Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition (2112.12916v1)

Abstract: Existing Scene Text Recognition (STR) methods typically use a LLM to optimize the joint probability of the 1D character sequence predicted by a visual recognition (VR) model, which ignore the 2D spatial context of visual semantics within and between character instances, making them not generalize well to arbitrary shape scene text. To address this issue, we make the first attempt to perform textual reasoning based on visual semantics in this paper. Technically, given the character segmentation maps predicted by a VR model, we construct a subgraph for each instance, where nodes represent the pixels in it and edges are added between nodes based on their spatial similarity. Then, these subgraphs are sequentially connected by their root nodes and merged into a complete graph. Based on this graph, we devise a graph convolutional network for textual reasoning (GTR) by supervising it with a cross-entropy loss. GTR can be easily plugged in representative STR models to improve their performance owing to better textual reasoning. Specifically, we construct our model, namely S-GTR, by paralleling GTR to the LLM in a segmentation-based STR baseline, which can effectively exploit the visual-linguistic complementarity via mutual learning. S-GTR sets new state-of-the-art on six challenging STR benchmarks and generalizes well to multi-linguistic datasets. Code is available at https://github.com/adeline-cs/GTR.

Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition

The research paper titled "Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition" addresses a significant challenge in the field of scene text recognition (STR): the inadequacy of current methodologies to account for spatial context, hindering their robustness against arbitrary shape scene texts. The authors introduce an innovative approach that enhances textual reasoning through visual semantics, leading to improved generalization in STR models, specifically for irregular and spatially fragmented text instances.

Methodology Overview

The proposed approach integrates a graph-based model termed the Graph-based Textual Reasoning (GTR) module into existing STR frameworks. This module leverages the inherent spatial relationships in the character segmentation maps generated by visual recognition (VR) models. Unlike traditional STR methods that rely heavily on 1D character sequences and LLMs for context, the GTR adds a 2D spatial dimension by constructing graphs where nodes represent pixels and edges denote spatial similarities. This construction allows for a two-level graph representation: subgraphs encapsulating character instance pixels, and complete graphs formed by linking these subgraphs via root nodes.

A Graph Convolutional Network (GCN) is then utilized to encode the constructed graphs, thereby introducing spatial context into the textual reasoning process. This network is trained with cross-entropy loss and integrated with a segmentation-based STR framework to form the proposed model, S-GTR. The dual-modality learning strategy of S-GTR—parallel linguistic reasoning through traditional LLMs and visual semantics through GTR—demonstrates improved performance across multiple STR benchmarks.

Experimental Results and Implications

The empirical evaluations indicate that S-GTR achieves state-of-the-art performance on six challenging STR datasets, including both regular and irregular text scenarios. The numerical results showcase significant improvements in recognition accuracy compared to baseline methods and demonstrate robustness across variable conditions such as diverse fonts, scales, orientations, and occlusions.

The implications of integrating spatial reasoning with linguistic context extend beyond immediate performance gains. It opens avenues for STR systems to more effectively handle complex real-world applications where text appearance can heavily influence recognition quality. By demonstrating a clear benefit of spatial context inclusion, this work sets a potential direction for future research in AI, particularly in developing multi-modal learning systems that harness additional contextual dimensions.

Future Directions

The research presents a promising start for enhancing STR models by incorporating visual semantics, but several open questions remain. Future studies might explore optimizing the fusion of features within the GTR module or adapting this architecture to non-latin scripts and multi-linguistic datasets. Additionally, the integration of advanced transformer-based LLMs with GTR could provide further understanding of the interplay between multi-contextual learning modalities.

Overall, this paper makes a substantial contribution to the STR field by challenging the conventional reliance on linguistic models alone for textual reasoning. The proposed GTR module embodies an elegant synthesis of visual and linguistic contexts, paving the way for next-generation STR models capable of unprecedented contextual understanding.