In billet production scenarios, the billet number often appears as a sequence composed of multi-line characters that may have arbitrary orientations, and its recognition is a particular industrial OCR (Optical Character Recognition) task. Currently, mainstream OCR methods primarily operate at the character or line level. When using these OCR methods for multi-line text recognition, the recognition results are first obtained for individual characters or single lines of text, and then the results are integrated into the sequence in top-to-bottom and left-to-right order. However, these recognition methods are designed for horizontally arranged multi-line text and are unsuitable for accurately identifying skewed or reversed billet numbers. In such cases, the sequential integration order becomes ineffective, and skewed characters are susceptible to misidentification, resulting in erroneous recognition. To solve these problems, we propose to treat the arbitrary-oriented multi-line sequence of characters as an integrated text unit and recognize the entire text unit to avoid additional sequential integration. To achieve this, we propose the integration attention to simultaneously focus on individual character features, positional information between characters, and global features of the sequence. This way, characters as well as sequence features can be adaptively extracted and recognized. We also propose the Cascaded Sample Module (CSM) to extract multiscale global features, alleviating the misidentification of skewed characters. Extensive experiments on multiple datasets demonstrate that our method can achieve state-of-the-art performance with more than 30 \(\%\) accuracy improvement.