Suzhou numerals, a specialized numerical no-tation system historically used in Chinese com-merce and accounting, played a pivotal role in financial transactions from the Song Dynasty to the early 20th century. Despite their his-torical significance, they remain largely absent from modern OCR benchmarks, limiting com-putational access to archival trade documents. This paper presents a curated dataset of 773 expert-annotated Suzhou numeral samples ex-tracted from late Qing-era trade ledgers. We provide a statistical analysis of character distri-butions, offering insights into their real-world usage in historical bookkeeping. Additionally, we evaluate baseline performance with hand-written text recognition (HTR) model, high-lighting the challenges of recognizing low-resource brush-written numerals. By introduc-ing this dataset and initial benchmark results, we aim to facilitate research in historical doc-umentation in ancient Chinese characters, ad-vancing the digitization of early Chinese finan-cial records. The dataset is publicly available at our huggingface hub, and our codebase can be accessed at our github repository.

Transformer-based pre-trained models with millions of parameters require large storage. Recent approaches tackle this shortcoming by training adapters, but these approaches still require a relatively large number of parameters. In this study, AdapterBias, a surprisingly simple yet effective adapter architecture, is proposed. AdapterBias adds a token-dependent shift to the hidden output of transformer layers to adapt to downstream tasks with only a vector and a linear layer. Extensive experiments are conducted to demonstrate the effectiveness of AdapterBias. The experiments show that our proposed method can dramatically reduce the trainable parameters compared to the previous works with a minimal decrease in task performances compared with fine-tuned pre-trained models. We further find that AdapterBias automatically learns to assign more significant representation shifts to the tokens related to the task in consideration.