ICFHR2018 Competition on Automated Text Recognition on a READ Dataset


Scoreboard

Position Name Method Info Submitter Affiliation Submitted before deadline Score
1 AU_gctc_2 A deeper version of the network used in AU_gctc_1 Mohamed Yousef Bassyouni Faculty of Computers and Information, Assiut University, Egypt 1
2 AU_gctc_1 - A CTC-trained network with custom architecture. - Staged elastic and perspective distortions were used to augment images. - Images were used as-is without the provided mask. - CTC-greedy decoding is used directly without LM or any other tricks A paper will present all the details of the method Mohamed Yousef Bassyouni Faculty of Computers and Information, Assiut University, Egypt 2
3 finalResultsLITIS OCR conv blstm ctc + data augmentation (inclination, scaling) with LM based on multigrams with interpolation scheme. Data augmentation in test + ROVER algorithm LITIS Laboratoire d’Informatique, du Traitement de l’Information et des Systèmes, France, Yann Soullard Laboratoire d’Informatique, du Traitement de l’Information et des Systèmes, France 3
4 US-UC3M_DA - The CNN-BLSTM-CTC architecture used in https://arxiv.org/pdf/1804.01527.pdf at ICFHR 2018 Conference, trained without any LM. Data Augmentation applied. José Carlos Aradillas Universidad de Sevilla and Universidad Carlos III de Madrid 4
5 OSU_Submission_8 A CNN-RNN model was trained with CTC. Data were augmented by both grid distortion (Wigington et al 2017) and rescaling, rotation and shearing. A model was trained on the general data before fine-tuning the whole model on the respective specific datasets with test-side augmentation. Oliver Nina, Russell Ault OSU 5
6 FinalOCRLM OCR convolutional + BLSTM, fine-tuned with data augmentation, stride adaptation, and using cross validation. Language Model interpolating between a writer LM and a language-based LM. Perplexity measure for language detection and for selecting the interpolation parameter. ROVER on test. LITIS Laboratoire d’Informatique, du Traitement de l’Information et des Systèmes, France, Yann Soullard Laboratoire d’Informatique, du Traitement de l’Information et des Systèmes, France 6
7 US-UC3M - The CNN-BLSTM-CTC architecture used in https://arxiv.org/pdf/1804.01527.pdf at ICFHR 2018 Conference, trained without any LM. José Carlos Aradillas Universidad de Sevilla and Universidad Carlos III de Madrid 7
8 UOB-PTECH-BASELINE02 UOB and ParisTech baseline system - No LM ParisTech Telecom ParisTech, France, and University of Bala- mand, Lebanon, Chafic Mokbel, Edgard Chammas Telecom ParisTech, France, and University of Bala- mand, Lebanon, University of Balamand 8
9 UOB-PTECH-BASELINE00 UOB and ParisTech baseline system ParisTech Telecom ParisTech, France, and University of Bala- mand, Lebanon, Chafic Mokbel, Edgard Chammas Telecom ParisTech, France, and University of Bala- mand, Lebanon, University of Balamand 9
10 11LMI conv 11 LMI raw LITIS Laboratoire d’Informatique, du Traitement de l’Information et des Systèmes, France, Yann Soullard Laboratoire d’Informatique, du Traitement de l’Information et des Systèmes, France 10
11 RESWLM Results with generic LM and then adapted. Joan Andreu Sanchez Universitat Politècnica de València 11
12 MDLSTM MDLSTM fed with grayscale images and a simple language model. Research Group in Pattern Recognition and Digital Image Processing (RPPDI), Byron L. D. Bezerra University of Pernambuco, Research Group in Pattern Recognition and Digital Image Processing (RPPDI), University of Pernambuco, Research Group in Pattern Recognition and Digital Image Processing (RPPDI) 12

News

May 16, 2018:
The competition remains open beyond the ICFHR deadline. Feel free to submit new methods.

April 15, 2018:
test data available

January 22, 2018:
competition is open and training data available

Important Dates

January 22, 2018:
competition opens

January 22, 2018:
training data available

April 15, 2018:
test data available

May 1, 2018:
deadline for submitting results on the test data

May 16, 2018:
provide a brief system description

August 5-8, 2018:
Results announced at ICFHR 2018





Organizers







Tobias Strauß

[University of Rostock, CITlab – Computational Intelligence Technology Lab] 

Gundram Leifert

[Computational Intelligence Technology Laboratory / CITlab, Rostock, Germany] 

READ Partner

Tobias Hodel

[Staatsarchiv des Kantons Zürich] 

Researcher in Digital Humanities and Digital History.