Large-scale, high-quality data is essential for advancing the reasoning capabilities of large language models (LLMs). As publicly available Internet data becomes increasingly scarce, synthetic data has emerged as a crucial research direction. However, existing data synthesis methods often suffer from limited scalability, insufficient sample diversity, and a tendency to overfit to seed data, which constrains their practical utility. In this paper, we present \textit{\textbf{GRIP}}, a \textbf{G}raph-based \textbf{R}easoning \textbf{I}nstruction \textbf{P}roducer that efficiently synthesizes high-quality and diverse reasoning instructions. \textit{GRIP} constructs a knowledge graph by extracting high-level concepts from seed data, and uniquely leverages both explicit and implicit relationships within the graph to drive large-scale and diverse instruction data synthesis, while employing open-source multi-model supervision to ensure data quality. We apply \textit{GRIP} to the critical and challenging domain of mathematical reasoning. Starting from a seed set of 7.5K math reasoning samples, we construct \textbf{GRIP-MATH}, a dataset containing 2.1 million synthesized question-answer pairs. Compared to similar synthetic data methods, \textit{GRIP} achieves greater scalability and diversity while also significantly reducing costs. On mathematical reasoning benchmarks, models trained with GRIP-MATH demonstrate substantial improvements over their base models and significantly outperform previous data synthesis methods.

Arbitrary-shaped text detection in natural images is a challenging task due to the complexity of the background and the diversity of text properties. The difficulty lies in two aspects: accurate separation of adjacent texts and sufficient text feature representation. To handle these problems, we consider text detection as instance segmentation and propose a novel text detection framework, which jointly learns semantic segmentation and a pixel affinity pyramid in a unified fully convolutional network. Specifically, the pixel affinity pyramid is proposed to encode multi-scale instance affiliation relationships of pixels, which is not only robust to varying shapes of text but also provides an accurate boundary description for separating closely located texts. In the inference phase, a simple but effective post-processing is presented to reconstruct text instances from the semantic segmentation results under the guidance of the learned pixel affinity pyramid, achieving good accuracy and efficiency. Furthermore, to enhance the representation of text features in the neural network, two modules — the Region Enhancement Module (REM) and Attentional Fusion Module (AFM) — are proposed. The REM models the semantic correlations of regional features to enhance the features from the text area, which effectively suppresses false-positive detection. The AFM adaptively fuses multi-scale textual information through an attention mechanism to obtain abundant text semantic features, which benefits multi-sized text detection. Extensive ablation experiments are conducted demonstrating the effectiveness of the REM and AFM. Evaluation results on standard benchmarks, including Total-Text, ICDAR2015, SCUT-CTW1500, and MSRA-TD500, show that our method surpasses most existing text detectors and achieves state-of-the-art performance, denoting its superior capability in detecting arbitrary-shaped texts.

The exploration of linguistic information promotes the development of scene text recognition task. Benefiting from the significance in parallel reasoning and global relationship capture, transformer-based language model (TLM) has achieved dominant performance recently. As a decoupled structure from the recognition process, we argue that TLM's capability is limited by the input low-quality visual prediction. To be specific: 1) The visual prediction with low character-wise accuracy increases the correction burden of TLM. 2) The inconsistent word length between visual prediction and original image provides a wrong language modeling guidance in TLM. In this paper, we propose a Progressive scEne Text Recognizer (PETR) to improve the capability of transformer-based language model by handling above two problems. Firstly, a Destruction Learning Module (DLM) is proposed to consider the linguistic information in the visual context. DLM introduces the recognition of destructed images with disordered patches in the training stage. Through guiding the vision model to restore patch orders and make word-level prediction on the destructed images, visual prediction with high character-wise accuracy is obtained by exploring inner relationship between the local visual patches. Secondly, a new Language Rectification Module (LRM) is proposed to optimize the word length for language guidance rectification. Through progressively implementing LRM in different language modeling steps, a novel progressive rectification network is constructed to handle some extremely challenging cases (e.g. distortion, occlusion, etc.). By utilizing DLM and LRM, PETR enhances the capability of transformer-based language model from a more general aspect, that is, focusing on the reduction of correction burden and rectification of language modeling guidance. Compared with parallel transformer-based methods, PETR obtains 1.0% and 0.8% improvement on regular and irregular datasets respectively while introducing only 1.7M additional parameters. The extensive experiments on both English and Chinese benchmarks demonstrate that PETR achieves the state-of-the-art results.

Benefiting from the popularity of deep learning theory, scene text detection algorithms have developed rapidly in recent years. Methods representing text region by text segmentation map are proved to capture arbitrary-shaped text in a more flexible and accurate way. However, such segmentation-based methods are prone to be disturbed by the text-like background patterns (like the fence, grass, etc.), which generally suffer from imprecise boundary detail problem. In this paper, LEMNet is proposed to handle the imprecise boundary problem by guiding the generation of text boundary based on a priori constraint. In the training stage, Boundary Segmentation Branch is firstly constructed to predict coarse boundary mask for each text instance. Then, through mapping pixels into an embedding space, the proposed Pixel Embedding Branch makes the embedding representation of boundary points learn to be more similar, meanwhile enlarging the characteristic distance between background points and boundary points. During inference, noise in the coarse boundary segmentation map can be effectively suppressed by a Noisy Point Suppression Algorithm among pixel embedding vectors. In this way, LEMNet can generate a more precise boundary description of text regions. To further enhance the distinguishability of boundary features, we propose a Context Enhancement Module to capture feature interactions in different representation subspaces, in which features are parallelly performed attention and concatenated to generate enhanced features. Extensive experiments are conducted over four challenging datasets, which demonstrate the effectiveness of LEMNet. Specifically, LEMNet achieves F-measure of 85.2%, 87.6% and 85.2% on CTW1500, Total-Text and MSRA-TD500 respectively, which is the latest SOTA.

Scene text detection has witnessed rapid development in recent years. However, there still exists two main challenges: 1) many methods suffer from false positives in their text representations; 2) the large scale variance of scene texts makes it hard for network to learn samples. In this paper, we propose the ContourNet, which effectively handles these two problems taking a further step toward accurate arbitrary-shaped text detection. At first, a scale-insensitive Adaptive Region Proposal Network (Adaptive-RPN) is proposed to generate text proposals by only focusing on the Intersection over Union (IoU) values between predicted and ground-truth bounding boxes. Then a novel Local Orthogonal Texture-aware Module (LOTM) models the local texture information of proposal features in two orthogonal directions and represents text region with a set of contour points. Considering that the strong unidirectional or weakly orthogonal activation is usually caused by the monotonous texture characteristic of false-positive patterns (e.g. streaks.), our method effectively suppresses these false positives by only outputting predictions with high response value in both orthogonal directions. This gives more accurate description of text regions. Extensive experiments on three challenging datasets (Total-Text, CTW1500 and ICDAR2015) verify that our method achieves the state-of-the-art performance. Code is available at https://github.com/wangyuxin87/ContourNet.