Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: Binarization-free OCR for historical documents using LSTM networks
High-Performance OCR for Printed English and Fraktur Using LSTM Networks
Segmentation-free handwritten Chinese text recognition with LSTM-RNN
Low-resource Post Processing of Noisy OCR Output for Historical Corpus Digitisation.
Combining multiple thresholding binarization values to improve OCR output
Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning
Optical Character Recognition by Open Source OCR Tool Tesseract: A Case Study
Adapting the Tesseract open source OCR engine for multilingual OCR
Binarized LSTM Language Model
A selectional auto-encoder approach for document image binarization
Accelerating and Compressing LSTM Based Model for Online Handwritten Chinese Character Recognition
A context-sensitive-chunk BPTT approach to training deep LSTM/BLSTM recurrent neural networks for offline handwriting recognition
Handwriting recognition in historical documents using very large vocabularies
An Adaptive Binarization Technique for Low Quality Historical Documents
iDocChip: A Configurable Hardware Architecture for Historical Document Image Processing: Percentile Based Binarization
Keyword Spotting in Online Handwritten Documents Containing Text and Non-text Using BLSTM Neural Networks
Performance Evaluation Methodology for Historical Document Image Binarization
Page Segmentation for Historical Handwritten Documents Using Fully Convolutional Networks
Image Preprocessing for Improving OCR Accuracy
An Optical Character Recognition
Hardware architecture of Bidirectional Long Short-Term Memory Neural Network for Optical Character Recognition
Pricing American options with the SABR model
CA-RNN: Using Context-Aligned Recurrent Neural Networks for Modeling Sentence Similarity
A segmentation-free approach to Arabic and Urdu OCR
Geometric Correspondence Network for Camera Motion Estimation
SparkR: Scaling R Programs with Spark