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Found 20 papers, 1 papers with code
Callico: a Versatile Open-Source Document Image Annotation Platform
This paper presents Callico, a web-based open source platform designed to simplify the annotation process in document recognition projects.
Revisiting N-Gram Models: Their Impact in Modern Neural Networks for Handwritten Text Recognition
In recent advances in automatic text recognition (ATR), deep neural networks have demonstrated the ability to implicitly capture language statistics, potentially reducing the need for traditional language models.
Improving Automatic Text Recognition with Language Models in the PyLaia Open-Source Library
PyLaia is one of the most popular open-source software for Automatic Text Recognition (ATR), delivering strong performance in terms of speed and accuracy.
Reading Order Independent Metrics for Information Extraction in Handwritten Documents
In this paper, we propose and publicly release a set of reading order independent metrics tailored to Information Extraction evaluation in handwritten documents.
The Socface Project: Large-Scale Collection, Processing, and Analysis of a Century of French Censuses
For this project, we developed a complete processing workflow: large-scale data collection from French departmental archives, collaborative annotation of documents, training of handwritten table text and structure recognition models, and mass processing of millions of images.
Handwritten Text Recognition from Crowdsourced Annotations
However, selecting training samples based on the degree of agreement between annotators introduces a bias in the training data and does not improve the results.
Ranked #1 on
Handwritten Text Recognition
on Belfort
How to Choose Pretrained Handwriting Recognition Models for Single Writer Fine-Tuning
Recent advancements in Deep Learning-based Handwritten Text Recognition (HTR) have led to models with remarkable performance on both modern and historical manuscripts in large benchmark datasets.
Large Scale Genealogical Information Extraction From Handwritten Quebec Parish Records
Verification of the birth and death acts from this sample shows that 74% of them are considered complete and valid.
Key-value information extraction from full handwritten pages
We propose a Transformer-based approach for information extraction from digitized handwritten documents.
SIMARA: a database for key-value information extraction from full pages
We propose a new database for information extraction from historical handwritten documents.
Ranked #1 on
Key Information Extraction
on SIMARA
Confidence Estimation for Object Detection in Document Images
In the active learning framework, the three first estimators show a significant improvement in performance for the detection of document physical pages and text lines compared to a random selection of images.
The LAM Dataset: A Novel Benchmark for Line-Level Handwritten Text Recognition
With the aim of fostering the research on this topic, in this paper we present the Ludovico Antonio Muratori (LAM) dataset, a large line-level HTR dataset of Italian ancient manuscripts edited by a single author over 60 years.
Robust Text Line Detection in Historical Documents: Learning and Evaluation Methods
We present a study conducted using three state-of-the-art systems Doc-UFCN, dhSegment and ARU-Net and show that it is possible to build generic models trained on a wide variety of historical document datasets that can correctly segment diverse unseen pages.
Including Keyword Position in Image-based Models for Act Segmentation of Historical Registers
However, the segmentation of complex documents into semantic regions is sometimes impossible relying only on visual features and recent models embed both visual and textual information.
Multiple Document Datasets Pre-training Improves Text Line Detection With Deep Neural Networks
In this paper, we introduce a fully convolutional network for the document layout analysis task.
HORAE: an annotated dataset of books of hours
We introduce in this paper a new dataset of annotated pages from books of hours, a type of handwritten prayer books owned and used by rich lay people in the late middle ages.
Hierarchical Text Segmentation for Medieval Manuscripts
In this paper, we address the segmentation of books of hours, Latin devotional manuscripts of the late Middle Ages, that exhibit challenging issues: a complex hierarchical entangled structure, variable content, noisy transcriptions with no sentence markers, and strong correlations between sections for which topical information is no longer sufficient to draw segmentation boundaries.
Full-Page Text Recognition: Learning Where to Start and When to Stop
Text line detection and localization is a crucial step for full page document analysis, but still suffers from heterogeneity of real life documents.
Curriculum Learning for Handwritten Text Line Recognition
Recurrent Neural Networks (RNN) have recently achieved the best performance in off-line Handwriting Text Recognition.
Dropout improves Recurrent Neural Networks for Handwriting Recognition
Recurrent neural networks (RNNs) with Long Short-Term memory cells currently hold the best known results in unconstrained handwriting recognition.
