ICML '06 Proceedings of the 23rd international conference on Machine learning
Neural Computation
An Overview of the Tesseract OCR Engine
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
A Novel Connectionist System for Unconstrained Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adapting the Tesseract open source OCR engine for multilingual OCR
Proceedings of the International Workshop on Multilingual OCR
Learning long-term dependencies with gradient descent is difficult
IEEE Transactions on Neural Networks
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Language models or recognition dictionaries are usually considered an essential step in OCR. However, using a language model complicates training of OCR systems, and it also narrows the range of texts that an OCR system can be used with. Recent results have shown that Long Short-Term Memory (LSTM) based OCR yields low error rates even without language modeling. In this paper, we explore the question to what extent LSTM models can be used for multilingual OCR without the use of language models. To do this, we measure cross-language performance of LSTM models trained on different languages. LSTM models show good promise to be used for language-independent OCR. The recognition errors are very low (around 1%) without using any language model or dictionary correction.