1,411 research outputs found
Recognizing Degraded Handwritten Characters
In this paper, Slavonic manuscripts from the 11th
century written in Glagolitic script are
investigated. State-of-the-art optical character recognition methods produce poor results
for degraded handwritten document images. This is largely due to a lack of suitable
results from basic pre-processing steps such as binarization and image segmentation.
Therefore, a new, binarization-free approach will be presented that is independent of
pre-processing deficiencies. It additionally incorporates local information in order to
recognize also fragmented or faded characters. The proposed algorithm consists of
two steps: character classification and character localization. Firstly scale invariant
feature transform features are extracted and classified using support vector machines.
On this basis interest points are clustered according to their spatial information. Then,
characters are localized and eventually recognized by a weighted voting scheme of
pre-classified local descriptors. Preliminary results show that the proposed system can
handle highly degraded manuscript images with background noise, e.g. stains, tears,
and faded characters
Handwriting Recognition of Historical Documents with few labeled data
Historical documents present many challenges for offline handwriting
recognition systems, among them, the segmentation and labeling steps. Carefully
annotated textlines are needed to train an HTR system. In some scenarios,
transcripts are only available at the paragraph level with no text-line
information. In this work, we demonstrate how to train an HTR system with few
labeled data. Specifically, we train a deep convolutional recurrent neural
network (CRNN) system on only 10% of manually labeled text-line data from a
dataset and propose an incremental training procedure that covers the rest of
the data. Performance is further increased by augmenting the training set with
specially crafted multiscale data. We also propose a model-based normalization
scheme which considers the variability in the writing scale at the recognition
phase. We apply this approach to the publicly available READ dataset. Our
system achieved the second best result during the ICDAR2017 competition
Handwritten and Printed Text Separation in Real Document
The aim of the paper is to separate handwritten and printed text from a real
document embedded with noise, graphics including annotations. Relying on
run-length smoothing algorithm (RLSA), the extracted pseudo-lines and
pseudo-words are used as basic blocks for classification. To handle this, a
multi-class support vector machine (SVM) with Gaussian kernel performs a first
labelling of each pseudo-word including the study of local neighbourhood. It
then propagates the context between neighbours so that we can correct possible
labelling errors. Considering running time complexity issue, we propose linear
complexity methods where we use k-NN with constraint. When using a kd-tree, it
is almost linearly proportional to the number of pseudo-words. The performance
of our system is close to 90%, even when very small learning dataset where
samples are basically composed of complex administrative documents.Comment: Machine Vision Applications (2013
BN-DRISHTI: Bangla Document Recognition through Instance-level Segmentation of Handwritten Text Images
Handwriting recognition remains challenging for some of the most spoken
languages, like Bangla, due to the complexity of line and word segmentation
brought by the curvilinear nature of writing and lack of quality datasets. This
paper solves the segmentation problem by introducing a state-of-the-art method
(BN-DRISHTI) that combines a deep learning-based object detection framework
(YOLO) with Hough and Affine transformation for skew correction. However,
training deep learning models requires a massive amount of data. Thus, we also
present an extended version of the BN-HTRd dataset comprising 786 full-page
handwritten Bangla document images, line and word-level annotation for
segmentation, and corresponding ground truths for word recognition. Evaluation
on the test portion of our dataset resulted in an F-score of 99.97% for line
and 98% for word segmentation. For comparative analysis, we used three external
Bangla handwritten datasets, namely BanglaWriting, WBSUBNdb_text, and ICDAR
2013, where our system outperformed by a significant margin, further justifying
the performance of our approach on completely unseen samples.Comment: Will be published under the Springer Springer Lecture Notes in
Computer Science (LNCS) series, as part of ICDAR WML 202
A Comparative study of Arabic handwritten characters invariant feature
This paper is practically interested in the unchangeable feature of Arabic
handwritten character. It presents results of comparative study achieved on
certain features extraction techniques of handwritten character, based on Hough
transform, Fourier transform, Wavelet transform and Gabor Filter. Obtained
results show that Hough Transform and Gabor filter are insensible to the
rotation and translation, Fourier Transform is sensible to the rotation but
insensible to the translation, in contrast to Hough Transform and Gabor filter,
Wavelets Transform is sensitive to the rotation as well as to the translation
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