1,228 research outputs found
A Bottom Up Procedure for Text Line Segmentation of Latin Script
In this paper we present a bottom up procedure for segmentation of text lines
written or printed in the Latin script. The proposed method uses a combination
of image morphology, feature extraction and Gaussian mixture model to perform
this task. The experimental results show the validity of the procedure.Comment: Accepted and presented at the IEEE conference "International
Conference on Advances in Computing, Communications and Informatics (ICACCI)
2017
Text Line Segmentation of Historical Documents: a Survey
There is a huge amount of historical documents in libraries and in various
National Archives that have not been exploited electronically. Although
automatic reading of complete pages remains, in most cases, a long-term
objective, tasks such as word spotting, text/image alignment, authentication
and extraction of specific fields are in use today. For all these tasks, a
major step is document segmentation into text lines. Because of the low quality
and the complexity of these documents (background noise, artifacts due to
aging, interfering lines),automatic text line segmentation remains an open
research field. The objective of this paper is to present a survey of existing
methods, developed during the last decade, and dedicated to documents of
historical interest.Comment: 25 pages, submitted version, To appear in International Journal on
Document Analysis and Recognition, On line version available at
http://www.springerlink.com/content/k2813176280456k3
MatriVasha: A Multipurpose Comprehensive Database for Bangla Handwritten Compound Characters
At present, recognition of the Bangla handwriting compound character has been
an essential issue for many years. In recent years there have been
application-based researches in machine learning, and deep learning, which is
gained interest, and most notably is handwriting recognition because it has a
tremendous application such as Bangla OCR. MatrriVasha, the project which can
recognize Bangla, handwritten several compound characters. Currently, compound
character recognition is an important topic due to its variant application, and
helps to create old forms, and information digitization with reliability. But
unfortunately, there is a lack of a comprehensive dataset that can categorize
all types of Bangla compound characters. MatrriVasha is an attempt to align
compound character, and it's challenging because each person has a unique style
of writing shapes. After all, MatrriVasha has proposed a dataset that intends
to recognize Bangla 120(one hundred twenty) compound characters that consist of
2552(two thousand five hundred fifty-two) isolated handwritten characters
written unique writers which were collected from within Bangladesh. This
dataset faced problems in terms of the district, age, and gender-based written
related research because the samples were collected that includes a verity of
the district, age group, and the equal number of males, and females. As of now,
our proposed dataset is so far the most extensive dataset for Bangla compound
characters. It is intended to frame the acknowledgment technique for
handwritten Bangla compound character. In the future, this dataset will be made
publicly available to help to widen the research.Comment: 19 fig, 2 tabl
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Use of colour for hand-filled form analysis and recognition
Colour information in form analysis is currently under utilised. As technology has advanced and computing costs have reduced, the processing of forms in colour has now become practicable. This paper describes a novel colour-based approach to the extraction of filled data from colour form images. Images are first quantised to reduce the colour complexity and data is extracted by examining the colour characteristics of the images. The improved performance of the proposed method has been verified by comparing the processing time, recognition rate, extraction precision and recall rate to that of an equivalent black and white system
Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition
Handwritten Text Recognition (HTR) is still a challenging problem because it
must deal with two important difficulties: the variability among writing
styles, and the scarcity of labelled data. To alleviate such problems,
synthetic data generation and data augmentation are typically used to train HTR
systems. However, training with such data produces encouraging but still
inaccurate transcriptions in real words. In this paper, we propose an
unsupervised writer adaptation approach that is able to automatically adjust a
generic handwritten word recognizer, fully trained with synthetic fonts,
towards a new incoming writer. We have experimentally validated our proposal
using five different datasets, covering several challenges (i) the document
source: modern and historic samples, which may involve paper degradation
problems; (ii) different handwriting styles: single and multiple writer
collections; and (iii) language, which involves different character
combinations. Across these challenging collections, we show that our system is
able to maintain its performance, thus, it provides a practical and generic
approach to deal with new document collections without requiring any expensive
and tedious manual annotation step.Comment: Accepted to WACV 202
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