197 research outputs found
A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks
Deep LSTM is an ideal candidate for text recognition. However text
recognition involves some initial image processing steps like segmentation of
lines and words which can induce error to the recognition system. Without
segmentation, learning very long range context is difficult and becomes
computationally intractable. Therefore, alternative soft decisions are needed
at the pre-processing level. This paper proposes a hybrid text recognizer using
a deep recurrent neural network with multiple layers of abstraction and long
range context along with a language model to verify the performance of the deep
neural network. In this paper we construct a multi-hypotheses tree architecture
with candidate segments of line sequences from different segmentation
algorithms at its different branches. The deep neural network is trained on
perfectly segmented data and tests each of the candidate segments, generating
unicode sequences. In the verification step, these unicode sequences are
validated using a sub-string match with the language model and best first
search is used to find the best possible combination of alternative hypothesis
from the tree structure. Thus the verification framework using language models
eliminates wrong segmentation outputs and filters recognition errors
Word Searching in Scene Image and Video Frame in Multi-Script Scenario using Dynamic Shape Coding
Retrieval of text information from natural scene images and video frames is a
challenging task due to its inherent problems like complex character shapes,
low resolution, background noise, etc. Available OCR systems often fail to
retrieve such information in scene/video frames. Keyword spotting, an
alternative way to retrieve information, performs efficient text searching in
such scenarios. However, current word spotting techniques in scene/video images
are script-specific and they are mainly developed for Latin script. This paper
presents a novel word spotting framework using dynamic shape coding for text
retrieval in natural scene image and video frames. The framework is designed to
search query keyword from multiple scripts with the help of on-the-fly
script-wise keyword generation for the corresponding script. We have used a
two-stage word spotting approach using Hidden Markov Model (HMM) to detect the
translated keyword in a given text line by identifying the script of the line.
A novel unsupervised dynamic shape coding based scheme has been used to group
similar shape characters to avoid confusion and to improve text alignment.
Next, the hypotheses locations are verified to improve retrieval performance.
To evaluate the proposed system for searching keyword from natural scene image
and video frames, we have considered two popular Indic scripts such as Bangla
(Bengali) and Devanagari along with English. Inspired by the zone-wise
recognition approach in Indic scripts[1], zone-wise text information has been
used to improve the traditional word spotting performance in Indic scripts. For
our experiment, a dataset consisting of images of different scenes and video
frames of English, Bangla and Devanagari scripts were considered. The results
obtained showed the effectiveness of our proposed word spotting approach.Comment: Multimedia Tools and Applications, Springe
Indiscapes: Instance Segmentation Networks for Layout Parsing of Historical Indic Manuscripts
Historical palm-leaf manuscript and early paper documents from Indian
subcontinent form an important part of the world's literary and cultural
heritage. Despite their importance, large-scale annotated Indic manuscript
image datasets do not exist. To address this deficiency, we introduce
Indiscapes, the first ever dataset with multi-regional layout annotations for
historical Indic manuscripts. To address the challenge of large diversity in
scripts and presence of dense, irregular layout elements (e.g. text lines,
pictures, multiple documents per image), we adapt a Fully Convolutional Deep
Neural Network architecture for fully automatic, instance-level spatial layout
parsing of manuscript images. We demonstrate the effectiveness of proposed
architecture on images from the Indiscapes dataset. For annotation flexibility
and keeping the non-technical nature of domain experts in mind, we also
contribute a custom, web-based GUI annotation tool and a dashboard-style
analytics portal. Overall, our contributions set the stage for enabling
downstream applications such as OCR and word-spotting in historical Indic
manuscripts at scale.Comment: Oral presentation at International Conference on Document Analysis
and Recognition (ICDAR) - 2019. For dataset, pre-trained networks and
additional details, visit project page at http://ihdia.iiit.ac.in
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