A Semi-automatic Annotation Scheme for Bangla Online Mixed Cursive Handwriting Samples

Bhattacharya, Ujjwal; Banerjee, Rajib; Baral, S.; De, Rajonya; Parui, Susanta Kumar

doi:10.1109/icfhr.2012.168

Cited by 10 publications

(2 citation statements)

References 15 publications

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“…Similarly, with the evident need for annotated data for offline handwritten text, especially with the shift towards Deep Learning based approaches for offline handwriting text recognition, it is not surprising that there has been a lot of research in this area too. There have been quite a few works that have proposed a systematic arrangement of stages to create a complete annotation engine for handwritten text, comprising of varying levels of automation [8][9]. However, a complete end-to-end pipeline to annotate handwritten text with very minimal human interaction is still considered to be a very challenging task as discussed in a part of a study by Ung et al [10].…”

Section: Related Workmentioning

confidence: 99%

An End-to-End, Interactive Deep Learning Based Annotation System for Cursive and Print English Handwritten Text

Guruprasad

Kumar

Vigneswaran

et al. 2021

Lecture Notes in Electrical Engineering

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With the surging inclination towards carrying out tasks on computational devices and digital mediums, any method that converts a task that was previously carried out manually, to a digitized version, is always welcome. Irrespective of the various documentation tasks that can be done online today, there are still many applications and domains where handwritten text is inevitable, which makes the digitization of handwritten documents a very essential task. Over the past decades, there has been extensive research on offline handwritten text recognition. In the recent past, most of these attempts have shifted to Machine learning and Deep learning based approaches. In order to design more complex and deeper networks, and ensure stellar performances, it is essential to have larger quantities of annotated data. Most of the databases present for offline handwritten text recognition today, have either been manually annotated or semi automatically annotated with a lot of manual involvement. These processes are very time consuming and prone to human errors. To tackle this problem, we present an innovative, complete end-to-end pipeline, that annotates offline handwritten manuscripts written in both print and cursive English, using Deep Learning and User Interaction techniques. This novel method, which involves an architectural combination of a detection system built upon a stateof-the-art text detection model, and a custom made Deep Learning model for the recognition system, is combined with an easy-to-use interactive interface, aiming to improve the accuracy of the detection, segmentation, serialization and recognition phases, in order to ensure high quality annotated data with minimal human interaction.

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Section: Related Workmentioning

confidence: 99%

An End-to-End, Interactive Deep Learning Based Annotation System for Cursive and Print English Handwritten Text

Guruprasad

Kumar

Vigneswaran

et al. 2021

Lecture Notes in Electrical Engineering

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“…Also, no such study could be found on detection of core region of either online or offline handwritten cursive words of an Indian script although these scripts need special attention due to their individual characteristics, which differ significantly from Latin or any other script. A few existing studies on online and offline handwritten cursive words of Indian scripts can be found in [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Detection of Core Region of Online Handwritten Bangla Word Samples

Baral

Bhattacharya

Chakraborty

et al. 2014

2014 14th International Conference on Frontiers in Handwriting Recognition

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Core region detection of handwritten cursive words is an important step towards their automatic recognition. Several preprocessing operations such as height normalization, slant estimation etc. are often based on this core region. This is particularly useful for word recognition of major Indian scripts, which have large character sets. The main parts of majority of these characters belong to the core region that is bounded above by a headline and bounded below by an imaginary base line. Only a few such characters or their parts appear either above or below the core region. A few approaches are available in the literature for detection of such a core region of offline handwritten word samples of Latin script. Also, a similar region is often determined for recognition of images of printed Indian scripts. However, none of these approaches have studied detection of core region of an unconstrained online handwritten word. In this article, we propose a novel method for detection of the core region of online handwritten word samples of Bangla, a major Indian script. For this we first perform smoothing on the samples and then segment a stroke into substrokes. We compute certain novel positional features from each such substroke. Using these features, a multilayer perceptron (MLP) is trained by backpropagation (BP) algorithm. On the basis of the output of the MLP, we determine the position of both the headline and the baseline. We have tested this approach on a recently developed large database of online unconstrained handwriting Bangla word samples. The proposed approach would also work on similar samples of Devanagari, another major Indian script. Experimental results are encouraging.I.

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A Simple and Effective Technique for Online Handwritten Bangla Character Recognition

Sen¹,

Sarkar

2015

Advances in Intelligent Systems and Computing

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A Semi-automatic Annotation Scheme for Bangla Online Mixed Cursive Handwriting Samples

Cited by 10 publications

References 15 publications

An End-to-End, Interactive Deep Learning Based Annotation System for Cursive and Print English Handwritten Text

An End-to-End, Interactive Deep Learning Based Annotation System for Cursive and Print English Handwritten Text

A Machine Learning Approach to Detection of Core Region of Online Handwritten Bangla Word Samples

A Simple and Effective Technique for Online Handwritten Bangla Character Recognition

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