Nougat: Neural Optical Understanding for Academic Documents (2308.13418v1)

Abstract: Scientific knowledge is predominantly stored in books and scientific journals, often in the form of PDFs. However, the PDF format leads to a loss of semantic information, particularly for mathematical expressions. We propose Nougat (Neural Optical Understanding for Academic Documents), a Visual Transformer model that performs an Optical Character Recognition (OCR) task for processing scientific documents into a markup language, and demonstrate the effectiveness of our model on a new dataset of scientific documents. The proposed approach offers a promising solution to enhance the accessibility of scientific knowledge in the digital age, by bridging the gap between human-readable documents and machine-readable text. We release the models and code to accelerate future work on scientific text recognition.

• The paper introduces the Nougat model, a Transformer-based approach that directly converts document images into structured markup.
• It leverages a comprehensive dataset and advanced augmentations to accurately preserve mathematical expressions and textual semantics.
• The approach works on both scanned and digital texts, enhancing accessibility and searchability of academic content.

Overview of "Nougat: Neural Optical Understanding for Academic Documents"

The paper "Nougat: Neural Optical Understanding for Academic Documents" by Blecher et al. introduces a novel approach for transcribing digital and scanned academic documents into machine-readable markup format. The authors present a Visual Transformer model named Nougat, which performs Optical Character Recognition (OCR) tasks on scientific documents, solving a critical challenge in the digital accessibility of academic texts, particularly in preserving the semantic structure of mathematical expressions.

Contributions

The paper's primary contributions include:

1. Creation of Nougat Model: The core innovation is a Transformer-based model that directly processes images of document pages to translate them into markup language without relying on traditional OCR tools or embedded text data.
2. Development and Release of a Dataset: The authors created a comprehensive dataset by pairing PDFs with source code from repositories like arXiv and PMC, which helped train the model to convert PDF documents accurately.
3. Universal Applicability to Scanned and Digital Texts: Nougat can handle both scanned and digitally-born documents, expanding the reach of OCR capabilities beyond digital PDFs and into the field of scanned text synthesis.

Methodology

The architecture of Nougat is based on an encoder-decoder Transformer paradigm. The encoder uses a Swin Transformer to process document images, dividing them into patches and transforming these into latent embeddings. The decoder, inspired by mBART, converts these embeddings into sequences of tokens representing the document's textual content in a structured markup format.

The authors employ sophisticated data augmentation techniques for improving the model's ability to generalize from digitally-born to scanned document inputs. Augmentations such as adding noise, blurring, and grid distortion mimic the imperfections found in scanned documents.

Results

The evaluation metrics used are edit distance, BLEU, METEOR, and F-measure across different text modalities—plain text, mathematical expressions, and tables. The results indicate that Nougat outperforms existing solutions like GROBID in converting PDFs to markup while preserving semantic information, particularly for mathematical expressions.

Nougat also addresses the common issue in Transformer models of generating repetitive sequences by introducing noise during training and monitoring the logit variance during inference to preemptively detect and manage these degeneracies.

Implications and Future Work

From a practical standpoint, Nougat enhances the accessibility of academic knowledge by making it easier to extract and search through scientific data that is otherwise trapped in non-parsable formats. Theoretically, it advances the field of visual document understanding (VDU) by demonstrating the feasibility of direct image-to-markup transcription without intermediary text extraction steps.

Although the model shows promising results, there are identified areas for future research. One notable limitation is the model's handling of full documents as isolated pages, which may lead to inconsistencies in document style across pages. Additionally, the generation speed poses a challenge when compared to traditional OCR tools, thereby paving the way for further optimization studies.

In conclusion, Nougat provides a substantial contribution to the field of scientific document analysis, with potential applications extending to accessibility technologies and document search engines. The released datasets and codebase set a foundation for subsequent improvements and adaptations in OCR technologies and beyond.