Due to the limited scale of multimodal table understanding (MTU) data, model performance is constrained. A straightforward approach is to use multimodal large language models to obtain more samples, but this may cause hallucinations, generate incorrect sample pairs, and cost significantly. To address the above issues, we design a simple yet effective synthesis framework that consists of two independent steps: table image rendering and table question and answer (Q&A) pairs generation. We use table codes (HTML, LaTeX, Markdown) to synthesize images and generate Q&A pairs with large language model (LLM). This approach leverages LLMs high concurrency and low cost to boost annotation efficiency and reduce expenses. By inputting code instead of images, LLMs can directly access the content and structure of the table, reducing hallucinations in table understanding and improving the accuracy of generated Q&A pairs. Finally, we synthesize a large-scale MTU dataset, SynTab, containing 636K images and 1.8M samples costing within $200 in US dollars. We further introduce a generalist tabular multimodal model, SynTab-LLaVA. This model not only effectively extracts local textual content within the table but also enables global modeling of relationships between cells. SynTab-LLaVA achieves SOTA performance on 21 out of 24 in-domain and out-of-domain benchmarks, demonstrating the effectiveness and generalization of our method. The Code is available at SynTab-LLaVA.

Existing scene text recognition (STR) methods struggle to recognize challenging texts, especially for artistic and severely distorted characters. The limitation lies in the insufficient exploration of character morphologies, including the monotonousness of widely used synthetic training data and the sensitivity of the model to character morphologies. To address these issues, inspired by the human learning process of viewing and summarizing, we facilitate the contrastive learning-based STR framework in a self-motivated manner by leveraging synthetic and real unlabeled data without any human cost. In the viewing process, to compensate for the simplicity of synthetic data and enrich character morphology diversity, we propose an Online Generation Strategy to generate background-free samples with diverse character styles. By excluding background noise distractions, the model is encouraged to focus on character morphology and generalize the ability to recognize complex samples when trained with only simple synthetic data. To boost the summarizing process, we theoretically demonstrate the derivation error in the previous character contrastive loss, which mistakenly causes the sparsity in the intra-class distribution and exacerbates ambiguity on challenging samples. Therefore, a new Character Unidirectional Alignment Loss is proposed to correct this error and unify the representation of the same characters in all samples by aligning the character features in the student model with the reference features in the teacher model. Extensive experiment results show that our method achieves SOTA performance (94.7\% and 70.9\% average accuracy on common benchmarks and Union14M-Benchmark). Code will be available.