May 10, 2024, 4:44 a.m. | William Watson, Bo Liu

cs.CV updates on

arXiv:2405.05260v1 Announce Type: new
Abstract: Table extraction has long been a pervasive problem in financial services. This is more challenging in the image domain, where content is locked behind cumbersome pixel format. Luckily, advances in deep learning for image segmentation, OCR, and sequence modeling provides the necessary heavy lifting to achieve impressive results. This paper presents an end-to-end pipeline for identifying, extracting and transcribing tabular content in image documents, while retaining the original spatial relations with high fidelity.

abstract advances arxiv deep learning documents domain extraction financial financial services format image locked modeling ocr pixel results segmentation services table table extraction type

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