SlideShare une entreprise Scribd logo

OCR processing with deep learning: Apply to Vietnamese documents

•
•
Viet-Trung TRAN
Viet-Trung TRAN

OCR processing with deep learning: Apply to Vietnamese documents

Technologie
1  sur  47
OCR PROCESSING WITH DEEP LEARNING: APPLY TO VIETNAMESE DOCUMENTS VIET-TRUNG TRAN, ANH PHI NGUYEN, KHUYEN NGUYEN
OUTLINE • OCR overview • History • Pipelining • Deep learning for OCR • Motivation • Connectionist temporal classification (CTC) network • LSTM + CTC for sequence recognition
WHAT IS OCR • Optical character recognition (optical character reader) (OCR) is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text
OCR TYPES • Optical Character Recognition (OCR) • Targets typewritten text, one character at a time • Optical Word Recognition (OWR) • Typewritten text, one word at a time • Intelligent Character Recognition (ICR) • Handwritten print script, one character at a time • Intelligent Word Recognition (IWR) • Handwritten, one word at a time
HISTORY OF OCR: TESSERACT OCR ENGINE TIMELINE
TESSERACT SYSTEM ARCHITECTURE
ARCHITECTURE [CONT’D]
ADAPTIVE THRESHOLDING
PAGE LAYOUT ANALYSIS Smith, Ray. "Hybrid page layout analysis via tab-stop detection." Document Analysis and Recognition, 2009. ICDAR'09. 10th International Conference on. IEEE, 2009.
IMAGE LEVEL PAGE LAYOUT ANALYSIS • Using the morphological processing from Leptonica • http://www.slideshare.net/versae/javier-de-larosacs9883-5912825
CONNECTED COMPONENT ANALYSIS
COLUMN FINDING
BLOCK FINDING
TESSERACT WORD RECOGNIZER http://www.slideshare.net/temsolin/2-architecture-anddatastructures
FEATURES AND WORD CLASSIFIER Classical character classification
SEGMENTATION GRAPH
CHAR SEGMENTATION, LANGUAGE MODEL AND BEAM SEARCH
OCR CHALLENGES 1. Fonts specifics Never overcome their ability to understand a limited numbers of fonts and page formats 2. Character bounding boxes 3. Extracting features unreliable 4. Slow performance
TESSERACT TUTORIAL @ DAS 2014
RECENT IMPROVEMENTS 1. Multilanguages 2. Full layout analysis 3. Table detection 4. Equation detection 5. Better language models 6. Hand-written text
LSTM FOR TEXT RECOGNITION
MOTIVATION • Segmentation is difficult for cursive or unconstrained text • R. Smith, “History of the Tesseract OCR engine: what worked and what didn’t ,” in DRR XX, San Francisco, USA, Feb. 2013. • there was not a single method proposed for OCR, that can achieve very low error rates without using aforementioned sophisticated post-processing techniques.
RESEARCH BREAKTHROUGH A. Graves, M. Liwicki, S. Fernandez, Bertolami, H. Bunke, and J. Schmidhuber, “A Novel Connectionist System for Unconstrained Handwriting Recognition,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 31, no. 5, pp. 855–868, May 2008.
TEXT LINE NORMALIZATION
TEXT LINE RECOGNITION
MOTIVATION • Real-world sequence learning task • OCR (Optical character recognition) • ASR (Automatic speech recognition) • Requires • prediction of sequences of labels from noisy, unsegmented input data • Recurrent neural networks (RNN) can be used for sequence learning, but ask for • pre-segmented training data • post-processing to transform outputs into label sequences
CONNECTIONIST TEMPORAL CLASSIFICATION (CTC) • Graves, Alex, et al. "Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks." Proceedings of the 23rd international conference on Machine learning. ACM, 2006. • WHAT CTC IS ALL ABOUT? •a novel method for training RNNs to label unsegmented sequences directly
THE SPEECH RECOGNITION PROBLEM
DYNAMIC TIME WRAPERING • Because the length of y might differ from (often longer than) l, so the inference of l from y is actually a dynamic time warping problem.
CONNECTIONIST TEMPORAL CLASSIFICATION • o transform the network outputs into a conditional probability distribution over label sequences • A CTC network has a softmax output layer with one more unit than there are labels in L • activations of the first |L| units are interpreted as the probabilities of observing the corresponding labels at particular times • activation of the extra unit is the probability of observing a ‘blank’, or no label
PREFIX SEARCH DECODING ON THE LABEL ALPHABET X,Y
LONG SHORT-TERM MEMORY (LSTM) • One type of RNN networks • RNN vanishing gradient problem • influence of a given input on the hidden layer, and therefore on the network output, either decays or blows up exponentially as it cycles around the network’s recurrent connections • LSTM is designed to address vanishing gradient problem • An LSTM hidden layer consists of recurrently connected subnets, called memory blocks • Each block contains a set of internal units, or cells, whose activation is controlled by three multiplicative gates: the input gate, forget gate and output gate
LSTM MEMORY BLOCK
FORGET GATE
INPUT GATE
OUTPUT GATE
LSTM -> CTC OUTPUT LAYER: OCR
DEMO TIME: OCR FOR VIETNAMESE DOCUMENTS Thank you!
REFERENCES - CREDITS • https://github.com/yiwangbaidu/notes/blob/master/CTC/CTC.pdf • http://colah.github.io/posts/2015-08-Understanding-LSTMs/ • Ray Smith. Everything you always wanted to know about Tesseract. Tesseract tutorial @ DAS 2014

Recommandé

Word2vec algorithm
Word2vec algorithmWord2vec algorithm
Word2vec algorithmAndrew Koo
 
An overview of gradient descent optimization algorithms
An overview of gradient descent optimization algorithms An overview of gradient descent optimization algorithms
An overview of gradient descent optimization algorithms Hakky St
 
Chapter 13 Linear Factor Models
Chapter 13 Linear Factor ModelsChapter 13 Linear Factor Models
Chapter 13 Linear Factor ModelsKyeongUkJang
 
SOLID Principles and Design Patterns
SOLID Principles and Design PatternsSOLID Principles and Design Patterns
SOLID Principles and Design PatternsGanesh Samarthyam
 
Deep Learning in Computer Vision
Deep Learning in Computer VisionDeep Learning in Computer Vision
Deep Learning in Computer VisionSungjoon Choi
 
Linear regression with gradient descent
Linear regression with gradient descentLinear regression with gradient descent
Linear regression with gradient descentSuraj Parmar
 
오토인코더의 모든 것
오토인코더의 모든 것오토인코더의 모든 것
오토인코더의 모든 것NAVER Engineering
 
Reinforcement Learning
Reinforcement LearningReinforcement Learning
Reinforcement Learningbutest
 

Recommandé

Word2vec algorithm
Word2vec algorithmWord2vec algorithm
Word2vec algorithmAndrew Koo
 
An overview of gradient descent optimization algorithms
An overview of gradient descent optimization algorithms An overview of gradient descent optimization algorithms
An overview of gradient descent optimization algorithms Hakky St
 
Chapter 13 Linear Factor Models
Chapter 13 Linear Factor ModelsChapter 13 Linear Factor Models
Chapter 13 Linear Factor ModelsKyeongUkJang
 
SOLID Principles and Design Patterns
SOLID Principles and Design PatternsSOLID Principles and Design Patterns
SOLID Principles and Design PatternsGanesh Samarthyam
 
Deep Learning in Computer Vision
Deep Learning in Computer VisionDeep Learning in Computer Vision
Deep Learning in Computer VisionSungjoon Choi
 
Linear regression with gradient descent
Linear regression with gradient descentLinear regression with gradient descent
Linear regression with gradient descentSuraj Parmar
 
오토인코더의 모든 것
오토인코더의 모든 것오토인코더의 모든 것
오토인코더의 모든 것NAVER Engineering
 
Reinforcement Learning
Reinforcement LearningReinforcement Learning
Reinforcement Learningbutest
 
Deep Learning Workflows: Training and Inference
Deep Learning Workflows: Training and InferenceDeep Learning Workflows: Training and Inference
Deep Learning Workflows: Training and InferenceNVIDIA
 
Deep Learning - A Literature survey
Deep Learning - A Literature surveyDeep Learning - A Literature survey
Deep Learning - A Literature surveyAkshay Hegde
 
Clean code: SOLID
Clean code: SOLIDClean code: SOLID
Clean code: SOLIDIndeema Software Inc.
 
Machine Learning Pipelines
Machine Learning PipelinesMachine Learning Pipelines
Machine Learning Pipelinesjeykottalam
 
Gradient descent optimizer
Gradient descent optimizerGradient descent optimizer
Gradient descent optimizerHojin Yang
 
General introduction to AI ML DL DS
General introduction to AI ML DL DSGeneral introduction to AI ML DL DS
General introduction to AI ML DL DSRoopesh Kohad
 
Optimization for Deep Learning
Optimization for Deep LearningOptimization for Deep Learning
Optimization for Deep LearningSebastian Ruder
 
그림 그리는 AI
그림 그리는 AI그림 그리는 AI
그림 그리는 AINAVER Engineering
 
Optimization in Deep Learning
Optimization in Deep LearningOptimization in Deep Learning
Optimization in Deep LearningYan Xu
 
A Review of Deep Contextualized Word Representations (Peters+, 2018)
A Review of Deep Contextualized Word Representations (Peters+, 2018)A Review of Deep Contextualized Word Representations (Peters+, 2018)
A Review of Deep Contextualized Word Representations (Peters+, 2018)Shuntaro Yada
 
PCA and LDA in machine learning
PCA and LDA in machine learningPCA and LDA in machine learning
PCA and LDA in machine learningAkhilesh Joshi
 
Tutorial on Deep Generative Models
Tutorial on Deep Generative Models Tutorial on Deep Generative Models
Tutorial on Deep Generative ModelsMLReview
 
Deep sarsa, Deep Q-learning, DQN
Deep sarsa, Deep Q-learning, DQNDeep sarsa, Deep Q-learning, DQN
Deep sarsa, Deep Q-learning, DQNEuijin Jeong
 
Blenderbot
BlenderbotBlenderbot
Blenderbottaeseon ryu
 
Wasserstein GAN 수학 이해하기 I
Wasserstein GAN 수학 이해하기 IWasserstein GAN 수학 이해하기 I
Wasserstein GAN 수학 이해하기 ISungbin Lim
 
[251] implementing deep learning using cu dnn
[251] implementing deep learning using cu dnn[251] implementing deep learning using cu dnn
[251] implementing deep learning using cu dnnNAVER D2
 
YOLO V6
YOLO V6YOLO V6
YOLO V6taeseon ryu
 
Deep Dive into Hyperparameter Tuning
Deep Dive into Hyperparameter TuningDeep Dive into Hyperparameter Tuning
Deep Dive into Hyperparameter TuningShubhmay Potdar
 
Managing dependencies with gradle
Managing dependencies with gradleManaging dependencies with gradle
Managing dependencies with gradleLiviu Tudor
 
An introduction to deep reinforcement learning
An introduction to deep reinforcement learningAn introduction to deep reinforcement learning
An introduction to deep reinforcement learningBig Data Colombia
 
Long Short Term Memory LSTM
Long Short Term Memory LSTMLong Short Term Memory LSTM
Long Short Term Memory LSTMAbdullah al Mamun
 
From neural networks to deep learning
From neural networks to deep learningFrom neural networks to deep learning
From neural networks to deep learningViet-Trung TRAN
 

Contenu connexe

Tendances

Deep Learning Workflows: Training and Inference
Deep Learning Workflows: Training and InferenceDeep Learning Workflows: Training and Inference
Deep Learning Workflows: Training and InferenceNVIDIA
 
Deep Learning - A Literature survey
Deep Learning - A Literature surveyDeep Learning - A Literature survey
Deep Learning - A Literature surveyAkshay Hegde
 
Machine Learning Pipelines
Machine Learning PipelinesMachine Learning Pipelines
Machine Learning Pipelinesjeykottalam
 
Gradient descent optimizer
Gradient descent optimizerGradient descent optimizer
Gradient descent optimizerHojin Yang
 
General introduction to AI ML DL DS
General introduction to AI ML DL DSGeneral introduction to AI ML DL DS
General introduction to AI ML DL DSRoopesh Kohad
 
Optimization for Deep Learning
Optimization for Deep LearningOptimization for Deep Learning
Optimization for Deep LearningSebastian Ruder
 
Optimization in Deep Learning
Optimization in Deep LearningOptimization in Deep Learning
Optimization in Deep LearningYan Xu
 
A Review of Deep Contextualized Word Representations (Peters+, 2018)
A Review of Deep Contextualized Word Representations (Peters+, 2018)A Review of Deep Contextualized Word Representations (Peters+, 2018)
A Review of Deep Contextualized Word Representations (Peters+, 2018)Shuntaro Yada
 
PCA and LDA in machine learning
PCA and LDA in machine learningPCA and LDA in machine learning
PCA and LDA in machine learningAkhilesh Joshi
 
Tutorial on Deep Generative Models
Tutorial on Deep Generative Models Tutorial on Deep Generative Models
Tutorial on Deep Generative ModelsMLReview
 
Deep sarsa, Deep Q-learning, DQN
Deep sarsa, Deep Q-learning, DQNDeep sarsa, Deep Q-learning, DQN
Deep sarsa, Deep Q-learning, DQNEuijin Jeong
 
Wasserstein GAN 수학 이해하기 I
Wasserstein GAN 수학 이해하기 IWasserstein GAN 수학 이해하기 I
Wasserstein GAN 수학 이해하기 ISungbin Lim
 
[251] implementing deep learning using cu dnn
[251] implementing deep learning using cu dnn[251] implementing deep learning using cu dnn
[251] implementing deep learning using cu dnnNAVER D2
 
Deep Dive into Hyperparameter Tuning
Deep Dive into Hyperparameter TuningDeep Dive into Hyperparameter Tuning
Deep Dive into Hyperparameter TuningShubhmay Potdar
 
Managing dependencies with gradle
Managing dependencies with gradleManaging dependencies with gradle
Managing dependencies with gradleLiviu Tudor
 
An introduction to deep reinforcement learning
An introduction to deep reinforcement learningAn introduction to deep reinforcement learning
An introduction to deep reinforcement learningBig Data Colombia
 

Tendances (20)

Deep Learning Workflows: Training and Inference
Deep Learning Workflows: Training and InferenceDeep Learning Workflows: Training and Inference
Deep Learning Workflows: Training and Inference
 
Deep Learning - A Literature survey
Deep Learning - A Literature surveyDeep Learning - A Literature survey
Deep Learning - A Literature survey
 
Clean code: SOLID
Clean code: SOLIDClean code: SOLID
Clean code: SOLID
 
Machine Learning Pipelines
Machine Learning PipelinesMachine Learning Pipelines
Machine Learning Pipelines
 
Gradient descent optimizer
Gradient descent optimizerGradient descent optimizer
Gradient descent optimizer
 
General introduction to AI ML DL DS
General introduction to AI ML DL DSGeneral introduction to AI ML DL DS
General introduction to AI ML DL DS
 
Optimization for Deep Learning
Optimization for Deep LearningOptimization for Deep Learning
Optimization for Deep Learning
 
그림 그리는 AI
그림 그리는 AI그림 그리는 AI
그림 그리는 AI
 
Optimization in Deep Learning
Optimization in Deep LearningOptimization in Deep Learning
Optimization in Deep Learning
 
A Review of Deep Contextualized Word Representations (Peters+, 2018)
A Review of Deep Contextualized Word Representations (Peters+, 2018)A Review of Deep Contextualized Word Representations (Peters+, 2018)
A Review of Deep Contextualized Word Representations (Peters+, 2018)
 
PCA and LDA in machine learning
PCA and LDA in machine learningPCA and LDA in machine learning
PCA and LDA in machine learning
 
Tutorial on Deep Generative Models
Tutorial on Deep Generative Models Tutorial on Deep Generative Models
Tutorial on Deep Generative Models
 
Deep sarsa, Deep Q-learning, DQN
Deep sarsa, Deep Q-learning, DQNDeep sarsa, Deep Q-learning, DQN
Deep sarsa, Deep Q-learning, DQN
 
Blenderbot
BlenderbotBlenderbot
Blenderbot
 
Wasserstein GAN 수학 이해하기 I
Wasserstein GAN 수학 이해하기 IWasserstein GAN 수학 이해하기 I
Wasserstein GAN 수학 이해하기 I
 
[251] implementing deep learning using cu dnn
[251] implementing deep learning using cu dnn[251] implementing deep learning using cu dnn
[251] implementing deep learning using cu dnn
 
YOLO V6
YOLO V6YOLO V6
YOLO V6
 
Deep Dive into Hyperparameter Tuning
Deep Dive into Hyperparameter TuningDeep Dive into Hyperparameter Tuning
Deep Dive into Hyperparameter Tuning
 
Managing dependencies with gradle
Managing dependencies with gradleManaging dependencies with gradle
Managing dependencies with gradle
 
An introduction to deep reinforcement learning
An introduction to deep reinforcement learningAn introduction to deep reinforcement learning
An introduction to deep reinforcement learning
 

Similaire à OCR processing with deep learning: Apply to Vietnamese documents

From neural networks to deep learning
From neural networks to deep learningFrom neural networks to deep learning
From neural networks to deep learningViet-Trung TRAN
 
Modi script character recognition
Modi script character recognitionModi script character recognition
Modi script character recognitionNeha Kulkarni
 
Tsinghua invited talk_zhou_xing_v2r0
Tsinghua invited talk_zhou_xing_v2r0Tsinghua invited talk_zhou_xing_v2r0
Tsinghua invited talk_zhou_xing_v2r0Joe Xing
 
Recurrent neural networks for sequence learning and learning human identity f...
Recurrent neural networks for sequence learning and learning human identity f...Recurrent neural networks for sequence learning and learning human identity f...
Recurrent neural networks for sequence learning and learning human identity f...SungminYou
 
Recurrent Neural Networks, LSTM and GRU
Recurrent Neural Networks, LSTM and GRURecurrent Neural Networks, LSTM and GRU
Recurrent Neural Networks, LSTM and GRUananth
 
Natural Language Processing Advancements By Deep Learning: A Survey
Natural Language Processing Advancements By Deep Learning: A SurveyNatural Language Processing Advancements By Deep Learning: A Survey
Natural Language Processing Advancements By Deep Learning: A SurveyRimzim Thube
 
[PR12] PR-050: Convolutional LSTM Network: A Machine Learning Approach for Pr...
[PR12] PR-050: Convolutional LSTM Network: A Machine Learning Approach for Pr...[PR12] PR-050: Convolutional LSTM Network: A Machine Learning Approach for Pr...
[PR12] PR-050: Convolutional LSTM Network: A Machine Learning Approach for Pr...Taegyun Jeon
 
Neural Network Language Models for Candidate Scoring in Multi-System Machine...
Neural Network Language Models for Candidate Scoring in Multi-System Machine... Neural Network Language Models for Candidate Scoring in Multi-System Machine...
Neural Network Language Models for Candidate Scoring in Multi-System Machine...Matīss ‎‎‎‎‎‎‎  
 
NextWordPrediction_ppt[1].pptx
NextWordPrediction_ppt[1].pptxNextWordPrediction_ppt[1].pptx
NextWordPrediction_ppt[1].pptxError471
 
Machine learning with R
Machine learning with RMachine learning with R
Machine learning with RMaarten Smeets
 
Tesseract OCR Engine
Tesseract OCR EngineTesseract OCR Engine
Tesseract OCR EngineRaghu nath
 
Deep Learning: Application & Opportunity
Deep Learning: Application & OpportunityDeep Learning: Application & Opportunity
Deep Learning: Application & OpportunityiTrain
 
Sequence Model pytorch at colab with gpu.pdf
Sequence Model pytorch at colab with gpu.pdfSequence Model pytorch at colab with gpu.pdf
Sequence Model pytorch at colab with gpu.pdfFEG
 
DSRLab seminar Introduction to deep learning
DSRLab seminar Introduction to deep learningDSRLab seminar Introduction to deep learning
DSRLab seminar Introduction to deep learningPoo Kuan Hoong
 
is2015_poster
is2015_posteris2015_poster
is2015_posterJan Svec
 
Deep learning fundamentals workshop
Deep learning fundamentals workshopDeep learning fundamentals workshop
Deep learning fundamentals workshopSatnam Singh
 
Building a Neural Machine Translation System From Scratch
Building a Neural Machine Translation System From ScratchBuilding a Neural Machine Translation System From Scratch
Building a Neural Machine Translation System From ScratchNatasha Latysheva
 
Post Quantum Cryptography: Technical Overview
Post Quantum Cryptography: Technical OverviewPost Quantum Cryptography: Technical Overview
Post Quantum Cryptography: Technical OverviewRamesh Nagappan
 

Similaire à OCR processing with deep learning: Apply to Vietnamese documents (20)

Long Short Term Memory LSTM
Long Short Term Memory LSTMLong Short Term Memory LSTM
Long Short Term Memory LSTM
 
From neural networks to deep learning
From neural networks to deep learningFrom neural networks to deep learning
From neural networks to deep learning
 
Modi script character recognition
Modi script character recognitionModi script character recognition
Modi script character recognition
 
Tsinghua invited talk_zhou_xing_v2r0
Tsinghua invited talk_zhou_xing_v2r0Tsinghua invited talk_zhou_xing_v2r0
Tsinghua invited talk_zhou_xing_v2r0
 
Recurrent neural networks for sequence learning and learning human identity f...
Recurrent neural networks for sequence learning and learning human identity f...Recurrent neural networks for sequence learning and learning human identity f...
Recurrent neural networks for sequence learning and learning human identity f...
 
Recurrent Neural Networks, LSTM and GRU
Recurrent Neural Networks, LSTM and GRURecurrent Neural Networks, LSTM and GRU
Recurrent Neural Networks, LSTM and GRU
 
Natural Language Processing Advancements By Deep Learning: A Survey
Natural Language Processing Advancements By Deep Learning: A SurveyNatural Language Processing Advancements By Deep Learning: A Survey
Natural Language Processing Advancements By Deep Learning: A Survey
 
[PR12] PR-050: Convolutional LSTM Network: A Machine Learning Approach for Pr...
[PR12] PR-050: Convolutional LSTM Network: A Machine Learning Approach for Pr...[PR12] PR-050: Convolutional LSTM Network: A Machine Learning Approach for Pr...
[PR12] PR-050: Convolutional LSTM Network: A Machine Learning Approach for Pr...
 
Neural Network Language Models for Candidate Scoring in Multi-System Machine...
Neural Network Language Models for Candidate Scoring in Multi-System Machine... Neural Network Language Models for Candidate Scoring in Multi-System Machine...
Neural Network Language Models for Candidate Scoring in Multi-System Machine...
 
NextWordPrediction_ppt[1].pptx
NextWordPrediction_ppt[1].pptxNextWordPrediction_ppt[1].pptx
NextWordPrediction_ppt[1].pptx
 
Machine learning with R
Machine learning with RMachine learning with R
Machine learning with R
 
Tesseract OCR Engine
Tesseract OCR EngineTesseract OCR Engine
Tesseract OCR Engine
 
Deep Learning: Application & Opportunity
Deep Learning: Application & OpportunityDeep Learning: Application & Opportunity
Deep Learning: Application & Opportunity
 
Sequence Model pytorch at colab with gpu.pdf
Sequence Model pytorch at colab with gpu.pdfSequence Model pytorch at colab with gpu.pdf
Sequence Model pytorch at colab with gpu.pdf
 
DSRLab seminar Introduction to deep learning
DSRLab seminar Introduction to deep learningDSRLab seminar Introduction to deep learning
DSRLab seminar Introduction to deep learning
 
is2015_poster
is2015_posteris2015_poster
is2015_poster
 
Deep learning fundamentals workshop
Deep learning fundamentals workshopDeep learning fundamentals workshop
Deep learning fundamentals workshop
 
131 133
131 133131 133
131 133
 
Building a Neural Machine Translation System From Scratch
Building a Neural Machine Translation System From ScratchBuilding a Neural Machine Translation System From Scratch
Building a Neural Machine Translation System From Scratch
 
Post Quantum Cryptography: Technical Overview
Post Quantum Cryptography: Technical OverviewPost Quantum Cryptography: Technical Overview
Post Quantum Cryptography: Technical Overview
 

Plus de Viet-Trung TRAN

Bắt đầu tìm hiểu về dữ liệu lớn như thế nào - 2017
Bắt đầu tìm hiểu về dữ liệu lớn như thế nào - 2017Bắt đầu tìm hiểu về dữ liệu lớn như thế nào - 2017
Bắt đầu tìm hiểu về dữ liệu lớn như thế nào - 2017Viet-Trung TRAN
 
Dynamo: Amazon’s Highly Available Key-value Store
Dynamo: Amazon’s Highly Available Key-value StoreDynamo: Amazon’s Highly Available Key-value Store
Dynamo: Amazon’s Highly Available Key-value StoreViet-Trung TRAN
 
Pregel: Hệ thống xử lý đồ thị lớn
Pregel: Hệ thống xử lý đồ thị lớnPregel: Hệ thống xử lý đồ thị lớn
Pregel: Hệ thống xử lý đồ thị lớnViet-Trung TRAN
 
Mapreduce simplified-data-processing
Mapreduce simplified-data-processingMapreduce simplified-data-processing
Mapreduce simplified-data-processingViet-Trung TRAN
 
Tìm kiếm needle trong Haystack: Hệ thống lưu trữ ảnh của Facebook
Tìm kiếm needle trong Haystack: Hệ thống lưu trữ ảnh của FacebookTìm kiếm needle trong Haystack: Hệ thống lưu trữ ảnh của Facebook
Tìm kiếm needle trong Haystack: Hệ thống lưu trữ ảnh của FacebookViet-Trung TRAN
 
giasan.vn real-estate analytics: a Vietnam case study
giasan.vn real-estate analytics: a Vietnam case studygiasan.vn real-estate analytics: a Vietnam case study
giasan.vn real-estate analytics: a Vietnam case studyViet-Trung TRAN
 
A Vietnamese Language Model Based on Recurrent Neural Network
A Vietnamese Language Model Based on Recurrent Neural NetworkA Vietnamese Language Model Based on Recurrent Neural Network
A Vietnamese Language Model Based on Recurrent Neural NetworkViet-Trung TRAN
 
A Vietnamese Language Model Based on Recurrent Neural Network
A Vietnamese Language Model Based on Recurrent Neural NetworkA Vietnamese Language Model Based on Recurrent Neural Network
A Vietnamese Language Model Based on Recurrent Neural NetworkViet-Trung TRAN
 
Large-Scale Geographically Weighted Regression on Spark
Large-Scale Geographically Weighted Regression on SparkLarge-Scale Geographically Weighted Regression on Spark
Large-Scale Geographically Weighted Regression on SparkViet-Trung TRAN
 
Recent progress on distributing deep learning
Recent progress on distributing deep learningRecent progress on distributing deep learning
Recent progress on distributing deep learningViet-Trung TRAN
 
success factors for project proposals
success factors for project proposalssuccess factors for project proposals
success factors for project proposalsViet-Trung TRAN
 
Paper@Soict2015: GPSInsights: towards a scalable framework for mining massive...
Paper@Soict2015: GPSInsights: towards a scalable framework for mining massive...Paper@Soict2015: GPSInsights: towards a scalable framework for mining massive...
Paper@Soict2015: GPSInsights: towards a scalable framework for mining massive...Viet-Trung TRAN
 
Introduction to BigData @TCTK2015
Introduction to BigData @TCTK2015Introduction to BigData @TCTK2015
Introduction to BigData @TCTK2015Viet-Trung TRAN
 
From decision trees to random forests
From decision trees to random forestsFrom decision trees to random forests
From decision trees to random forestsViet-Trung TRAN
 
Recommender systems: Content-based and collaborative filtering
Recommender systems: Content-based and collaborative filteringRecommender systems: Content-based and collaborative filtering
Recommender systems: Content-based and collaborative filteringViet-Trung TRAN
 
3 - Finding similar items
3 - Finding similar items3 - Finding similar items
3 - Finding similar itemsViet-Trung TRAN
 
Dimensionality reduction: SVD and its applications
Dimensionality reduction: SVD and its applicationsDimensionality reduction: SVD and its applications
Dimensionality reduction: SVD and its applicationsViet-Trung TRAN
 

Plus de Viet-Trung TRAN (20)

Bắt đầu tìm hiểu về dữ liệu lớn như thế nào - 2017
Bắt đầu tìm hiểu về dữ liệu lớn như thế nào - 2017Bắt đầu tìm hiểu về dữ liệu lớn như thế nào - 2017
Bắt đầu tìm hiểu về dữ liệu lớn như thế nào - 2017
 
Dynamo: Amazon’s Highly Available Key-value Store
Dynamo: Amazon’s Highly Available Key-value StoreDynamo: Amazon’s Highly Available Key-value Store
Dynamo: Amazon’s Highly Available Key-value Store
 
Pregel: Hệ thống xử lý đồ thị lớn
Pregel: Hệ thống xử lý đồ thị lớnPregel: Hệ thống xử lý đồ thị lớn
Pregel: Hệ thống xử lý đồ thị lớn
 
Mapreduce simplified-data-processing
Mapreduce simplified-data-processingMapreduce simplified-data-processing
Mapreduce simplified-data-processing
 
Tìm kiếm needle trong Haystack: Hệ thống lưu trữ ảnh của Facebook
Tìm kiếm needle trong Haystack: Hệ thống lưu trữ ảnh của FacebookTìm kiếm needle trong Haystack: Hệ thống lưu trữ ảnh của Facebook
Tìm kiếm needle trong Haystack: Hệ thống lưu trữ ảnh của Facebook
 
giasan.vn real-estate analytics: a Vietnam case study
giasan.vn real-estate analytics: a Vietnam case studygiasan.vn real-estate analytics: a Vietnam case study
giasan.vn real-estate analytics: a Vietnam case study
 
Giasan.vn @rstars
Giasan.vn @rstarsGiasan.vn @rstars
Giasan.vn @rstars
 
A Vietnamese Language Model Based on Recurrent Neural Network
A Vietnamese Language Model Based on Recurrent Neural NetworkA Vietnamese Language Model Based on Recurrent Neural Network
A Vietnamese Language Model Based on Recurrent Neural Network
 
A Vietnamese Language Model Based on Recurrent Neural Network
A Vietnamese Language Model Based on Recurrent Neural NetworkA Vietnamese Language Model Based on Recurrent Neural Network
A Vietnamese Language Model Based on Recurrent Neural Network
 
Large-Scale Geographically Weighted Regression on Spark
Large-Scale Geographically Weighted Regression on SparkLarge-Scale Geographically Weighted Regression on Spark
Large-Scale Geographically Weighted Regression on Spark
 
Recent progress on distributing deep learning
Recent progress on distributing deep learningRecent progress on distributing deep learning
Recent progress on distributing deep learning
 
success factors for project proposals
success factors for project proposalssuccess factors for project proposals
success factors for project proposals
 
GPSinsights poster
GPSinsights posterGPSinsights poster
GPSinsights poster
 
Paper@Soict2015: GPSInsights: towards a scalable framework for mining massive...
Paper@Soict2015: GPSInsights: towards a scalable framework for mining massive...Paper@Soict2015: GPSInsights: towards a scalable framework for mining massive...
Paper@Soict2015: GPSInsights: towards a scalable framework for mining massive...
 
Deep learning for nlp
Deep learning for nlpDeep learning for nlp
Deep learning for nlp
 
Introduction to BigData @TCTK2015
Introduction to BigData @TCTK2015Introduction to BigData @TCTK2015
Introduction to BigData @TCTK2015
 
From decision trees to random forests
From decision trees to random forestsFrom decision trees to random forests
From decision trees to random forests
 
Recommender systems: Content-based and collaborative filtering
Recommender systems: Content-based and collaborative filteringRecommender systems: Content-based and collaborative filtering
Recommender systems: Content-based and collaborative filtering
 
3 - Finding similar items
3 - Finding similar items3 - Finding similar items
3 - Finding similar items
 
Dimensionality reduction: SVD and its applications
Dimensionality reduction: SVD and its applicationsDimensionality reduction: SVD and its applications
Dimensionality reduction: SVD and its applications
 

Dernier

Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreternaman860154
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slidespraypatel2
 
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxMalak Abu Hammad
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptxHampshireHUG
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slidevu2urc
 
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesUnblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesSinan KOZAK
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...Neo4j
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Enterprise Knowledge
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Igalia
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonetsnaman860154
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfEnterprise Knowledge
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationMichael W. Hawkins
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsEnterprise Knowledge
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Scriptwesley chun
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘RTylerCroy
 

Dernier (20)

Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slides
 
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
Neo4j - How KGs are shaping the future of Generative AI at AWS Summit London ...
 
The Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptxThe Codex of Business Writing Software for Real-World Solutions 2.pptx
The Codex of Business Writing Software for Real-World Solutions 2.pptx
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
 
Unblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen FramesUnblocking The Main Thread Solving ANRs and Frozen Frames
Unblocking The Main Thread Solving ANRs and Frozen Frames
 
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
How to convert PDF to text with Nanonets
How to convert PDF to text with NanonetsHow to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
 
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
 

OCR processing with deep learning: Apply to Vietnamese documents

  • 1. OCR PROCESSING WITH DEEP LEARNING: APPLY TO VIETNAMESE DOCUMENTS VIET-TRUNG TRAN, ANH PHI NGUYEN, KHUYEN NGUYEN
  • 2. OUTLINE • OCR overview • History • Pipelining • Deep learning for OCR • Motivation • Connectionist temporal classification (CTC) network • LSTM + CTC for sequence recognition
  • 3. WHAT IS OCR • Optical character recognition (optical character reader) (OCR) is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine-encoded text
  • 4. OCR TYPES • Optical Character Recognition (OCR) • Targets typewritten text, one character at a time • Optical Word Recognition (OWR) • Typewritten text, one word at a time • Intelligent Character Recognition (ICR) • Handwritten print script, one character at a time • Intelligent Word Recognition (IWR) • Handwritten, one word at a time
  • 5. HISTORY OF OCR: TESSERACT OCR ENGINE TIMELINE
  • 9. PAGE LAYOUT ANALYSIS Smith, Ray. "Hybrid page layout analysis via tab-stop detection." Document Analysis and Recognition, 2009. ICDAR'09. 10th International Conference on. IEEE, 2009.
  • 10. IMAGE LEVEL PAGE LAYOUT ANALYSIS • Using the morphological processing from Leptonica • http://www.slideshare.net/versae/javier-de-larosacs9883-5912825
  • 15. FEATURES AND WORD CLASSIFIER Classical character classification
  • 17. CHAR SEGMENTATION, LANGUAGE MODEL AND BEAM SEARCH
  • 18. OCR CHALLENGES 1. Fonts specifics Never overcome their ability to understand a limited numbers of fonts and page formats 2. Character bounding boxes 3. Extracting features unreliable 4. Slow performance
  • 20. RECENT IMPROVEMENTS 1. Multilanguages 2. Full layout analysis 3. Table detection 4. Equation detection 5. Better language models 6. Hand-written text
  • 21. LSTM FOR TEXT RECOGNITION
  • 22. MOTIVATION • Segmentation is difficult for cursive or unconstrained text • R. Smith, “History of the Tesseract OCR engine: what worked and what didn’t ,” in DRR XX, San Francisco, USA, Feb. 2013. • there was not a single method proposed for OCR, that can achieve very low error rates without using aforementioned sophisticated post-processing techniques.
  • 23. RESEARCH BREAKTHROUGH A. Graves, M. Liwicki, S. Fernandez, Bertolami, H. Bunke, and J. Schmidhuber, “A Novel Connectionist System for Unconstrained Handwriting Recognition,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 31, no. 5, pp. 855–868, May 2008.
  • 26. MOTIVATION • Real-world sequence learning task • OCR (Optical character recognition) • ASR (Automatic speech recognition) • Requires • prediction of sequences of labels from noisy, unsegmented input data • Recurrent neural networks (RNN) can be used for sequence learning, but ask for • pre-segmented training data • post-processing to transform outputs into label sequences
  • 27. CONNECTIONIST TEMPORAL CLASSIFICATION (CTC) • Graves, Alex, et al. "Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks." Proceedings of the 23rd international conference on Machine learning. ACM, 2006. • WHAT CTC IS ALL ABOUT? •a novel method for training RNNs to label unsegmented sequences directly
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34. DYNAMIC TIME WRAPERING • Because the length of y might differ from (often longer than) l, so the inference of l from y is actually a dynamic time warping problem.
  • 35.
  • 36.
  • 37. CONNECTIONIST TEMPORAL CLASSIFICATION • o transform the network outputs into a conditional probability distribution over label sequences • A CTC network has a softmax output layer with one more unit than there are labels in L • activations of the first |L| units are interpreted as the probabilities of observing the corresponding labels at particular times • activation of the extra unit is the probability of observing a ‘blank’, or no label
  • 38. PREFIX SEARCH DECODING ON THE LABEL ALPHABET X,Y
  • 39.
  • 40. LONG SHORT-TERM MEMORY (LSTM) • One type of RNN networks • RNN vanishing gradient problem • influence of a given input on the hidden layer, and therefore on the network output, either decays or blows up exponentially as it cycles around the network’s recurrent connections • LSTM is designed to address vanishing gradient problem • An LSTM hidden layer consists of recurrently connected subnets, called memory blocks • Each block contains a set of internal units, or cells, whose activation is controlled by three multiplicative gates: the input gate, forget gate and output gate
  • 45. LSTM -> CTC OUTPUT LAYER: OCR
  • 46. DEMO TIME: OCR FOR VIETNAMESE DOCUMENTS Thank you!
  • 47. REFERENCES - CREDITS • https://github.com/yiwangbaidu/notes/blob/master/CTC/CTC.pdf • http://colah.github.io/posts/2015-08-Understanding-LSTMs/ • Ray Smith. Everything you always wanted to know about Tesseract. Tesseract tutorial @ DAS 2014

Notes de l'éditeur

  1. http://www.slideshare.net/100002968637682/seminar-p2
  2. http://www.slideshare.net/DocuFi/improve-ocr-accuracy-with-cleanup?qid=a439a1d4-ad96-4fb3-b376-9b301e58674b&v=default&b=&from_search=13
  3. http://www.slideshare.net/temsolin/6-char-segmentation