A High Performance Domain Specific Ocr For Bangla Script

Hasnat, Md. Abul; Habib, S. M. Murtoza; Khan, Mumit

doi:10.1007/978-1-4020-8737-0_31

Cited by 28 publications

(7 citation statements)

References 11 publications

(25 reference statements)

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“…Several other people laid the foundation for Bengali handwritten character recognition, in those work Ray and Chatterjee did the first work. After that, many more researchers did other methods, improving the performance of Bangla Handwritten Character Recognition (HCR) like Hasant et al [2] proposed an HCR capable of classifying both printed and handwritten characters by using Discrete Cosine Transform method over the input image and Hidden Markov Model (HMM) for character classification. Wen et al [3] proposed a Bangla numerals recognition method using Principal Component Analysis and Support Vector Machines, but those are using traditional machine learning methods.…”

Section: Previous Workmentioning

confidence: 99%

AKHCRNet: Bengali Handwritten Character Recognition Using Deep Learning

Roy¹

2020

Preprint

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I propose a state of the art deep neural architectural solution for handwritten character recognition for Bengali alphabets, compound alphabets as well as numerical digits that achieves state-of-the-art accuracy 96.8% in just 11 epochs. Similar work has been done before by Chatterjee, Dutta, et al. 2019[1] but they achieved 96.12% accuracy in about 47 epochs. The deep neural architecture used in that paper was fairly large considering the inclusion of the weights of the ResNet 50 model which is a 50 layer Residual Network. This proposed model achieves higher accuracy as compared to any previous work & in a little number of epochs. ResNet50 is a good model trained on the ImageNet dataset, but I propose an HCR network that is trained from the scratch on Bengali characters without the "Ensemble Learning" that can outperform previous architectures.

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Section: Previous Workmentioning

confidence: 99%

AKHCRNet: Bengali Handwritten Character Recognition Using Deep Learning

Roy¹

2020

Preprint

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“…Bangla OCR by the Center for Research Bangla Language Processing (CRBLP) which converts written text or pictures into editable Unicode text [13], has weaknesses as it is for a restricted scope. However, greater efficiency has been accomplished by the OCR scheme for Bangla [14] and the web handwritten OCR for Bangla [15].…”

Section: Related Workmentioning

confidence: 99%

Untitled

2019

IJNLC

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Information Retrieval System is an effective process that helps a user to trace relevant information by Natural Language Processing (NLP). In this research paper, we have presented present an algorithmic Information Retrieval System(BIRS) based on information and the system is significant mathematically and statistically. This paper is demonstrated by two algorithms for finding out the lemmatization of Bengali words such as Trie and Dictionary Based Search by Removing Affix (DBSRA) as well as compared with Edit Distance for the exact lemmatization. We have presented the Bengali Anaphora resolution system using the Hobbs' algorithm to get the correct expression of information. As the actions of questions answering algorithms, the TF-IDF and Cosine Similarity are developed to find out the accurate answer from the documents. In this study, we have introduced a Bengali Language Toolkit (BLTK) and Bengali Language Expression (BRE) that make the easiest implication of our task. We have also developed Bengali root word's corpus, synonym word's corpus, stop word's corpus and gathered 672 articles from the popular Bengali newspapers 'The Daily Prothom Alo' which is our inserted information. For testing this system, we have created 19335 questions from the introduced information and got 97.22% accurate answer.

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“…Hidden Markov Model (HMM): The feature vector sequence is processed using left-to-right continuous density HMMs [15]. One of the important features of HMM is the capability to model sequential dependencies.…”

Section: A Hidden Markov Model-based Recognition Systemmentioning

confidence: 99%

Date field extraction from handwritten documents using HMMs

Mandal

Roy

Palz

et al. 2015

2015 13th International Conference on Document Analysis and Recognition (ICDAR)

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Automatic document interpretation and retrieval is an important task to access handwritten digitized document repositories. In documents, the date is an important field and it has various applications such as date-wise document indexing/retrieval. In this paper, a framework has been proposed for automatic date field extraction from handwritten documents. In order to design the system, sliding window-wise Local Gradient Histogram (LGH)-based features and a character-level Hidden Markov Model (HMM)-based approach have been applied for segmentation and recognition. Individual date components such as month-word (month written in word form i.e. January, Jan, etc.), numeral, punctuation or contraction categories are segmented and labelled from a text line. Next, a Histogram of Gradient (HoG)-based features and a Support Vector Machine (SVM)based classifier have been used to refine the results obtained from the HMM-based recognition system. Subsequently, both numeric and semi-numeric regular expressions of date patterns have been considered for undertaking date pattern extraction in labelled components. The experiments are performed on an English document dataset and the encouraging results obtained from the approach indicate the effectiveness of the proposed system.

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A High Performance Domain Specific Ocr For Bangla Script

Cited by 28 publications

References 11 publications

AKHCRNet: Bengali Handwritten Character Recognition Using Deep Learning

AKHCRNet: Bengali Handwritten Character Recognition Using Deep Learning

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Date field extraction from handwritten documents using HMMs

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