6,465 research outputs found
Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition
Online handwritten Chinese text recognition (OHCTR) is a challenging problem
as it involves a large-scale character set, ambiguous segmentation, and
variable-length input sequences. In this paper, we exploit the outstanding
capability of path signature to translate online pen-tip trajectories into
informative signature feature maps using a sliding window-based method,
successfully capturing the analytic and geometric properties of pen strokes
with strong local invariance and robustness. A multi-spatial-context fully
convolutional recurrent network (MCFCRN) is proposed to exploit the multiple
spatial contexts from the signature feature maps and generate a prediction
sequence while completely avoiding the difficult segmentation problem.
Furthermore, an implicit language model is developed to make predictions based
on semantic context within a predicting feature sequence, providing a new
perspective for incorporating lexicon constraints and prior knowledge about a
certain language in the recognition procedure. Experiments on two standard
benchmarks, Dataset-CASIA and Dataset-ICDAR, yielded outstanding results, with
correct rates of 97.10% and 97.15%, respectively, which are significantly
better than the best result reported thus far in the literature.Comment: 14 pages, 9 figure
Unconstrained Scene Text and Video Text Recognition for Arabic Script
Building robust recognizers for Arabic has always been challenging. We
demonstrate the effectiveness of an end-to-end trainable CNN-RNN hybrid
architecture in recognizing Arabic text in videos and natural scenes. We
outperform previous state-of-the-art on two publicly available video text
datasets - ALIF and ACTIV. For the scene text recognition task, we introduce a
new Arabic scene text dataset and establish baseline results. For scripts like
Arabic, a major challenge in developing robust recognizers is the lack of large
quantity of annotated data. We overcome this by synthesising millions of Arabic
text images from a large vocabulary of Arabic words and phrases. Our
implementation is built on top of the model introduced here [37] which is
proven quite effective for English scene text recognition. The model follows a
segmentation-free, sequence to sequence transcription approach. The network
transcribes a sequence of convolutional features from the input image to a
sequence of target labels. This does away with the need for segmenting input
image into constituent characters/glyphs, which is often difficult for Arabic
script. Further, the ability of RNNs to model contextual dependencies yields
superior recognition results.Comment: 5 page
Some Approaches to the Recognition of Handwritten Numerals
The present work deals with the recognition of handwritten isolated numerals by utilizing a recent approach, which aims at tackling the variability in the writing styles. A two pronged approach involving pre-classification and the recognition has been followed in this paper. For the pre-classification of numerals, two approaches have been presented. The first is heuristic based and the second is stroke based. A recent feature extraction method, namely sector data method, which takes care of variability in the handwritten numerals, has been incorporated into the system thus the variability involved in the writing styles of different individuals is taken care of by extracting features from the sector based approach. The back propagation neural networks have been used in the recognition process using the features extracted from the sector-based approach. On the basis of recognition rates obtained with samples written by different individuals, it is concluded that the sector based approach is better suited for the recognition of numerals when pre-classification is made on the basis of strokes
Arabic cursive text recognition from natural scene images
© 2019 by the authors. This paper presents a comprehensive survey on Arabic cursive scene text recognition. The recent years' publications in this field have witnessed the interest shift of document image analysis researchers from recognition of optical characters to recognition of characters appearing in natural images. Scene text recognition is a challenging problem due to the text having variations in font styles, size, alignment, orientation, reflection, illumination change, blurriness and complex background. Among cursive scripts, Arabic scene text recognition is contemplated as a more challenging problem due to joined writing, same character variations, a large number of ligatures, the number of baselines, etc. Surveys on the Latin and Chinese script-based scene text recognition system can be found, but the Arabic like scene text recognition problem is yet to be addressed in detail. In this manuscript, a description is provided to highlight some of the latest techniques presented for text classification. The presented techniques following a deep learning architecture are equally suitable for the development of Arabic cursive scene text recognition systems. The issues pertaining to text localization and feature extraction are also presented. Moreover, this article emphasizes the importance of having benchmark cursive scene text dataset. Based on the discussion, future directions are outlined, some of which may provide insight about cursive scene text to researchers
A new hybrid convolutional neural network and eXtreme gradient boosting classifier for recognizing handwritten Ethiopian characters
Handwritten character recognition has been profoundly studied for many years in the field of pattern recognition. Due to its vast practical applications and financial implications, handwritten character recognition is still an important research area. In this research, the Handwritten Ethiopian Character Recognition (HECR) dataset has been prepared to train the model. The images in the HECR dataset were organized with more than one color pen RGB main spaces that have been size normalized to 28 × 28 pixels. The dataset is a combination of scripts (Fidel in Ethiopia), numerical representations, punctuations, tonal symbols, combining symbols, and special characters. These scripts have been used to write ancient histories, science, and arts of Ethiopia and Eritrea. In this study, a hybrid model of two super classifiers: Convolutional Neural Network (CNN) and eXtreme Gradient Boosting (XGBoost) is proposed for classification. In this integrated model, CNN works as a trainable automatic feature extractor from the raw images and XGBoost takes the extracted features as an input for recognition and classification. The output error rates of the hybrid model and CNN with a fully connected layer are compared. A 0.4630 and 0.1612 error rates are achieved in classifying the handwritten testing dataset images, respectively. Thus XGBoost as a classifier performs a better result than the traditional fully connected layer
NeuroWrite: Predictive Handwritten Digit Classification using Deep Neural Networks
The rapid evolution of deep neural networks has revolutionized the field of
machine learning, enabling remarkable advancements in various domains. In this
article, we introduce NeuroWrite, a unique method for predicting the
categorization of handwritten digits using deep neural networks. Our model
exhibits outstanding accuracy in identifying and categorising handwritten
digits by utilising the strength of convolutional neural networks (CNNs) and
recurrent neural networks (RNNs).In this article, we give a thorough
examination of the data preparation methods, network design, and training
methods used in NeuroWrite. By implementing state-of-the-art techniques, we
showcase how NeuroWrite can achieve high classification accuracy and robust
generalization on handwritten digit datasets, such as MNIST. Furthermore, we
explore the model's potential for real-world applications, including digit
recognition in digitized documents, signature verification, and automated
postal code recognition. NeuroWrite is a useful tool for computer vision and
pattern recognition because of its performance and adaptability.The
architecture, training procedure, and evaluation metrics of NeuroWrite are
covered in detail in this study, illustrating how it can improve a number of
applications that call for handwritten digit classification. The outcomes show
that NeuroWrite is a promising method for raising the bar for deep neural
network-based handwritten digit recognition.Comment: 6 pages, 10 figure
- …