Search Results for author:
Found 12 papers, 8 papers with code
Rescoring Sequence-to-Sequence Models for Text Line Recognition with CTC-Prefixes
The CTC confidences are computed on the encoder while the Transformer is only used for character-wise S2S decoding.
Mixed Model OCR Training on Historical Latin Script for Out-of-the-Box Recognition and Finetuning
Training a more specialized model for some unseen Early Modern Latin books starting from our mixed model led to a CER of 1. 47%, an improvement of up to 50% compared to training from scratch and up to 30% compared to training from the aforementioned standard model.
Optimizing small BERTs trained for German NER
Currently, the most widespread neural network architecture for training language models is the so called BERT which led to improvements in various Natural Language Processing (NLP) tasks.
OCR4all -- An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings
Nevertheless, in the last few years great progress has been made in the area of historical OCR, resulting in several powerful open-source tools for preprocessing, layout recognition and segmentation, character recognition and post-processing.
Optical Character Recognition
Optical Character Recognition (OCR)
State of the Art Optical Character Recognition of 19th Century Fraktur Scripts using Open Source Engines
In this paper we evaluate Optical Character Recognition (OCR) of 19th century Fraktur scripts without book-specific training using mixed models, i. e. models trained to recognize a variety of fonts and typesets from previously unseen sources.
Optical Character Recognition
Optical Character Recognition (OCR)
Calamari - A High-Performance Tensorflow-based Deep Learning Package for Optical Character Recognition
Optical Character Recognition (OCR) on contemporary and historical data is still in the focus of many researchers.
Optical Character Recognition
Optical Character Recognition (OCR)
Improving OCR Accuracy on Early Printed Books using Deep Convolutional Networks
This paper proposes a combination of a convolutional and a LSTM network to improve the accuracy of OCR on early printed books.
Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning
We combine three methods which significantly improve the OCR accuracy of OCR models trained on early printed books: (1) The pretraining method utilizes the information stored in already existing models trained on a variety of typesets (mixed models) instead of starting the training from scratch.
Transfer Learning for OCRopus Model Training on Early Printed Books
The evaluation on seven early printed books showed that training from the Latin mixed model reduces the average amount of errors by 43% and 26%, respectively compared to training from scratch with 60 and 150 lines of ground truth, respectively.
Leaf Identification Using a Deep Convolutional Neural Network
Convolutional neural networks (CNNs) have become popular especially in computer vision in the last few years because they achieved outstanding performance on different tasks, such as image classifications.
Improving OCR Accuracy on Early Printed Books by utilizing Cross Fold Training and Voting
Experiments on seven early printed books show that the proposed method outperforms the standard approach considerably by reducing the amount of errors by up to 50% and more.
Fully Convolutional Neural Networks for Page Segmentation of Historical Document Images
For evaluation of this model we introduce a novel metric that is independent of ambiguous ground truth called Foreground Pixel Accuracy (FgPA).
