Search Results for author:
Found 10 papers, 3 papers with code
OCR, Classification & Machine Translation (OCCAM)
The OCCAM project (Optical Character recognition, ClassificAtion & Machine Translation) aims at integrating the CEF (Connecting Europe Facility) Automated Translation service with image classification, Translation Memories (TMs), Optical Character Recognition (OCR), and Machine Translation (MT).
Towards Writing Style Adaptation in Handwriting Recognition
We experimented with various placements and settings of WSB and contrastively pre-trained embeddings.
Finetuning Is a Surprisingly Effective Domain Adaptation Baseline in Handwriting Recognition
In many machine learning tasks, a large general dataset and a small specialized dataset are available.
SoftCTC -- Semi-Supervised Learning for Text Recognition using Soft Pseudo-Labels
This paper explores semi-supervised training for sequence tasks, such as Optical Character Recognition or Automatic Speech Recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Importance of Textlines in Historical Document Classification
The line-level system significantly improves results in script and font classification and in the dating task.
AT-ST: Self-Training Adaptation Strategy for OCR in Domains with Limited Transcriptions
This paper addresses text recognition for domains with limited manual annotations by a simple self-training strategy.
TS-Net: OCR Trained to Switch Between Text Transcription Styles
Users of OCR systems, from different institutions and scientific disciplines, prefer and produce different transcription styles.
Page Layout Analysis System for Unconstrained Historic Documents
Extraction of text regions and individual text lines from historic documents is necessary for automatic transcription.
Brno Mobile OCR Dataset
We introduce the Brno Mobile OCR Dataset (B-MOD) for document Optical Character Recognition from low-quality images captured by handheld mobile devices.
Technical Report: Image Captioning with Semantically Similar Images
This report presents our submission to the MS COCO Captioning Challenge 2015.
