CNN Based Transfer Learning for Scene Script Identification

Abstract Script identification at character level in handwritten documents is a challenging task for Gurumukhi and Latin scripts due to the presence of slightly similar, quite similar or at times confusing character pairs. Hence, it is found to be inadequate to use single feature set or just traditional feature sets and classifier in processing the handwritten documents. Due to the evolution of deep learning, the importance of traditional feature extraction approaches is somewhere neglected which is considered in this paper. This paper investigates machine learning and deep learning ensemble approaches at feature extraction and classification level for script identification. The approach here is: i. combining traditional and deep learning based features ii. evaluating various ensemble approaches using individual and combined feature sets to perform script identification iii. evaluating the pre-trained deep networks using transfer learning for script identification ’iv. finding the best combination of feature set and classifiers for script identification. Three different kinds of traditional features like Gabor filter, Gray Level Co-Occurrence Matrix (GLCM), Histograms of Oriented Gradiants (HOG) are employed. For deep learning pretrained deep networks like VGG19, ResNet50 and LeNet5 have been used as feature extractor. These individual and combined features are trained using classifiers like Support Vector Machines (SVM) , K nearest neighbor (KNN), Random Forest (rf) etc. Further many ensemble approaches like Voting,Boosting and Bagging are evaluated for script classification. Exhaustive experimental work resulted into the highest accuracy of 98.82% with features extracted from ResNet50 using transfer learning and bagging based ensemble classifier which is higher as compared to previously reported work.

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Indices of transfer: Learning can transfer but still be specific

PsycEXTRA Dataset ◽

10.1037/e520592012-493 ◽

2010 ◽

Author(s):

Erica L. Wohldmann ◽

Alice F. Healy

Keyword(s):

Transfer Learning

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A transfer learning based microstructure reconstruction approach using microstructural correlation functions

10.26226/morressier.5f5f8e69aa777f8ba5bd604c ◽

2020 ◽

Author(s):

Ashwini Gupta

Keyword(s):

Transfer Learning ◽

Correlation Functions ◽

Microstructure Reconstruction

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Molecular Generation Targeting Desired Electronic Properties via Deep Generative Models

10.26434/chemrxiv.9913865.v2 ◽

2019 ◽

Author(s):

Qi Yuan ◽

Alejandro Santana-Bonilla ◽

Martijn Zwijnenburg ◽

Kim Jelfs

Keyword(s):

Neural Network ◽

Electronic Properties ◽

Transfer Learning ◽

Recurrent Neural Network ◽

Chemical Space ◽

Generative Models ◽

Molecular Features ◽

Donor Acceptor ◽

Homo Lumo ◽

Training Sets

<p>The chemical space for novel electronic donor-acceptor oligomers with targeted properties was explored using deep generative models and transfer learning. A General Recurrent Neural Network model was trained from the ChEMBL database to generate chemically valid SMILES strings. The parameters of the General Recurrent Neural Network were fine-tuned via transfer learning using the electronic donor-acceptor database from the Computational Material Repository to generate novel donor-acceptor oligomers. Six different transfer learning models were developed with different subsets of the donor-acceptor database as training sets. We concluded that electronic properties such as HOMO-LUMO gaps and dipole moments of the training sets can be learned using the SMILES representation with deep generative models, and that the chemical space of the training sets can be efficiently explored. This approach identified approximately 1700 new molecules that have promising electronic properties (HOMO-LUMO gap <2 eV and dipole moment <2 Debye), 6-times more than in the original database. Amongst the molecular transformations, the deep generative model has learned how to produce novel molecules by trading off between selected atomic substitutions (such as halogenation or methylation) and molecular features such as the spatial extension of the oligomer. The method can be extended as a plausible source of new chemical combinations to effectively explore the chemical space for targeted properties.</p>

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Metode Diplomatik dalam Mengidentifikasi Kandungan Isi Naskah Gulungan Berbahan Daluang Koleksi Candi Cangkuang

Panggung ◽

10.26742/panggung.v22i4.66 ◽

2012 ◽

Vol 22 (4) ◽

Author(s):

Tedi Permadi

Keyword(s):

Direct Observation ◽

Relevant Literature ◽

Script Identification ◽

Identification Techniques

ABSTRACTThis paper presents the results of the identification of rolled manuscripts made of daluang using diplomatic method. This method aims at getting the authenticity of the script based on the information that accompanies the text with the internal evidence contained in the manuscript. In terms of script identification techniques, diplomatic method utilizes direct observation techniques, assisted by other descriptions of contemporary manuscript as an evidence and support of the relevant literature. The use of diplomatic method in identifying rolled manuscripts produces the characteristics of the material, the literacy/language used in the text, and the editorial lapses contained in the text, but the identity of the author or the copyist and the time of the writing or copying manuscripts could not be found.Keywords: Manuscript identification, daluang, diplomatic method ABSTRAKTulisan ini menyajikan hasil identifikasi naskah gulungan berbahan daluang dengan menggunakan metode diplomatik. Metode diplomatik bertujuan untuk mendapatkan keaslian naskah berdasarkan informasi yang ada di dalam teks dengan bukti internal yang terkandung dalam naskah tersebut. Dalam hal teknik identifikasi naskah, metode diplomatik memanfaatkan teknik observasi langsung, dibantu dengan deskripsi dari naskah kontemporer lain sebagai bukti dan pendukung literatur yang relevan. Penggunaan metode diplomatik dalam mengidentifikasi naskah gulungan menghasilkan karakteristik material, huruf/bahasa yang digunakan dalam teks, dan penyimpangan editorial yang terkandung dalam teks, tetapi tidak bisa menemukan identitas penulis atau penyalin dan waktu penulisan atau penyalinan naskah.Kata kunci: Identifikasi naskah, daluang, metode diplomatik

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Learning from a Neighbor: Adapting a Japanese Parser for Korean Through Feature Transfer Learning

10.3115/v1/w14-4202 ◽

2014 ◽

Author(s):

Hiroshi Kanayama ◽

Youngja Park ◽

Yuta Tsuboi ◽

Dongmook Yi

Keyword(s):

Transfer Learning

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Deep Learning for text in limted data settings

10.36227/techrxiv.12100692 ◽

2020 ◽

Author(s):

Pathikkumar Patel ◽

Bhargav Lad ◽

Jinan Fiaidhi

Keyword(s):

Machine Learning ◽

Time Series ◽

Deep Learning ◽

Sentiment Analysis ◽

Transfer Learning ◽

Text Classification ◽

State Of The Art ◽

Time Series Forecasting ◽

Text Data ◽

Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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