Efficient, Lexicon-Free OCR using Deep Learning
Contrary to popular belief, Optical Character Recognition (OCR) remains a challenging problem when text occurs in unconstrained environments, like natural scenes, due to geometrical distortions, complex backgrounds, and diverse fonts. In this paper, we present a segmentation-free OCR system that combines deep learning methods, synthetic training data generation, and data augmentation techniques. We render synthetic training data using large text corpora and over 2000 fonts. To simulate text occurring in complex natural scenes, we augment extracted samples with geometric distortions and with a proposed data augmentation technique - alpha-compositing with background textures. Our models employ a convolutional neural network encoder to extract features from text images. Inspired by the recent progress in neural machine translation and language modeling, we examine the capabilities of both recurrent and convolutional neural networks in modeling the interactions between input elements.
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationDeep LearningLanguage ModelingLanguage ModellingMachine TranslationOptical Character RecognitionOptical Character Recognition (OCR)TranslationSimilar Papers 제목 키워드 기반
Data-driven grapheme-to-phoneme representations for a lexicon-free text-to-speech
Grapheme-to-Phoneme (G2P) is an essential first step in any modern, high-quality Text-to-Speech (TTS) system. Most of the current G2P systems rely on carefully hand-crafted lexicons developed by experts. This poses a two…
Self-Supervised Learningtext-to-speechText to SpeechWho Needs Words? Lexicon-Free Speech Recognition
Lexicon-free speech recognition naturally deals with the problem of out-of-vocabulary (OOV) words. In this paper, we show that character-based language models (LM) can perform as well as word-based LMs for speech recogni…
speech-recognitionSpeech RecognitionConfidence Prediction for Lexicon-Free OCR
Having a reliable accuracy score is crucial for real world applications of OCR, since such systems are judged by the number of false readings. Lexicon-based OCR systems, which deal with what is essentially a multi-class …
General ClassificationMulti-class ClassificationOptical Character Recognition (OCR)PredictionFrench parsing enhanced with a word clustering method based on a syntactic lexicon
This article evaluates the integration of data extracted from a French syntactic lexicon, the Lexicon-Grammar (Gross, 1994), into a probabilistic parser. We show that by applying clustering methods on verbs of the French…
A Free/Open-Source Morphological Transducer for Western Armenian
We present a free/open-source morphological transducer for Western Armenian, an endangered and low-resource Indo-European language. The transducer has virtually complete coverage of the language’s inflectional morphology…