Transformer-based HTR for Historical Documents

2022-03-21 · Phillip Benjamin Ströbel, Simon Clematide, Martin Volk, Tobias Hodel

We apply the TrOCR framework to real-world, historical manuscripts and show that TrOCR per se is a strong model, ideal for transfer learning. TrOCR has been trained on English only, but it can adapt to other languages that use the Latin alphabet fairly easily and with little training material. We compare TrOCR against a SOTA HTR framework (Transkribus) and show that it can beat such systems. This finding is essential since Transkribus performs best when it has access to baseline information, which is not needed at all to fine-tune TrOCR.

📄 PDF Abstract BibTeX arXiv:2203.11008

Code (1)

EriCongMa/awesome-transformer-ocr paddle

Tasks

HTRTransfer Learning

Methods 이 논문이 사용한 방법론

Multi-Head Attention 설명 없음

Attention 설명 없음

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

Residual Connection 설명 없음

Position-Wise Feed-Forward Layer 설명 없음

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named-entity-recognition Named Entity Recognition Named Entity Recognition (NER)NER+2

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Named entity recognition is of high interest to digital humanities, in particular when mining historical documents. Although the task is mature in the field of NLP, results of contemporary models are not satisfactory on …

named-entity-recognition Named Entity Recognition Named Entity Recognition (NER)Optical Character Recognition (OCR)

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named-entity-recognition Named Entity Recognition Named Entity Recognition (NER)NER+1

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Robust Document Layout Analysis (DLA) is critical for the automated processing and understanding of historical documents with complex page organizations. This paper benchmarks five state-of-the-art object detection archi…

Document Layout Analysis object-detection Object Detection

Counterfactual Query Rewriting to Use Historical Relevance Feedback

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When a retrieval system receives a query it has encountered before, previous relevance feedback, such as clicks or explicit judgments can help to improve retrieval results. However, the content of a previously relevant d…

counterfactual Retrieval