2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR)最新文献

Deep learning based isolated Arabic scene character recognition 基于深度学习的孤立阿拉伯场景字符识别

2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR) Pub Date : 2017-04-22 DOI: 10.1109/ASAR.2017.8067758

S. Ahmed, S. Naz, M. I. Razzak, Rubiyah Yousaf

引用次数: 46

Unconstrained scene text and video text recognition for Arabic script 不受约束的场景文本和视频文本识别的阿拉伯脚本

2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR) Pub Date : 2017-04-03 DOI: 10.1109/ASAR.2017.8067754

Mohit Jain, Minesh Mathew, C. V. Jawahar

{"title":"Unconstrained scene text and video text recognition for Arabic script","authors":"Mohit Jain, Minesh Mathew, C. V. Jawahar","doi":"10.1109/ASAR.2017.8067754","DOIUrl":"https://doi.org/10.1109/ASAR.2017.8067754","url":null,"abstract":"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 synthesizing 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.","PeriodicalId":224611,"journal":{"name":"2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123475678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 41

Multi-layer recurrent neural network based offline Arabic handwriting recognition 基于多层递归神经网络的离线阿拉伯手写识别

2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR) Pub Date : 2017-04-03 DOI: 10.1109/ASAR.2017.8067749

Liren Chen, Ruijie Yan, Liangrui Peng, Akio Furuhata, Xiaoqing Ding

引用次数: 5

Letter-based classification of Arabic scripts style in ancient Arabic manuscripts: Preliminary results 古代阿拉伯手稿中阿拉伯文字风格的字母分类:初步结果

2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR) Pub Date : 2017-04-03 DOI: 10.1109/ASAR.2017.8067767

Kalthoum Adam, S. Al-Maadeed, A. Bouridane

引用次数: 13

A survey on handwriting recognition based on the trajectory recovery technique 基于轨迹恢复技术的手写识别研究进展

2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR) Pub Date : 2017-04-03 DOI: 10.1109/ASAR.2017.8067762

Zouhaira Noubigh, M. Kherallah

引用次数: 9

Feature selection for an improved Parkinson's disease identification based on handwriting 基于笔迹的改进帕金森病识别的特征选择

2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR) Pub Date : 2017-04-01 DOI: 10.1109/ASAR.2017.8067759

C. Taleb, M. Khachab, C. Mokbel, Laurence Likforman-Sulem

{"title":"Feature selection for an improved Parkinson's disease identification based on handwriting","authors":"C. Taleb, M. Khachab, C. Mokbel, Laurence Likforman-Sulem","doi":"10.1109/ASAR.2017.8067759","DOIUrl":"https://doi.org/10.1109/ASAR.2017.8067759","url":null,"abstract":"Parkinson's disease (PD) is a neurological disorder associated with a progressive decline in motor skills, speech, and cognitive processes. Since the diagnosis of Parkinson's disease is difficult, researchers have worked to develop a support tool based on algorithms to separate healthy controls from PD patients. Online handwriting analysis is one of the methods that can be used to diagnose PD. The purpose of this study is to find a subset of handwriting features suitable for efficiently identifying subjects with PD. Data was taken from PDMultiMC database collected in Lebanon, and consisting of 16 medicated PD patients and 16 age matched controls. Seven handwriting tasks were collected such as copying patterns, copying words in Arabic, and writing full names. For each task kinematic and spatio-temporal, pressure, energy, entropy, and intrinsic features were extracted. Feature selection was done in two stages; the first stage selected a subset using statistical analysis, and the second step selected the most relevant features of this subset by a suboptimal approach. The selected features were fed to a support vector machine classifier with RBF kernel, whose aim is to identify the subjects suffering from PD. The accuracy of the classification of PD was as high as 96.875%, with sensitivity and specificity equal to 93.75 % and 100% respectively. The results as well as the selected features suggest that handwriting can be a valuable marker as a PD diagnosis tool.","PeriodicalId":224611,"journal":{"name":"2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129092666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 32

Dynamic Bayesian networks for handwritten Arabic word recognition 动态贝叶斯网络手写阿拉伯语单词识别

2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR) Pub Date : 2017-04-01 DOI: 10.1109/ASAR.2017.8067769

N. Ghanmi, Ahmad Montaser Awal, Nihel Kooli

引用次数: 4

Image and text correction using language models 使用语言模型进行图像和文本校正

2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR) Pub Date : 2017-04-01 DOI: 10.1109/ASAR.2017.8067779

Ido Kissos, N. Dershowitz

引用次数: 3

Stroke width exploitation to improve automatic recognition of Arabic handwritten texts 利用笔画宽度改进阿拉伯语手写文本的自动识别

2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR) Pub Date : 2017-04-01 DOI: 10.1109/ASAR.2017.8067763

E. Chammas, Laurence Likforman-Sulem, C. Mokbel

{"title":"Stroke width exploitation to improve automatic recognition of Arabic handwritten texts","authors":"E. Chammas, Laurence Likforman-Sulem, C. Mokbel","doi":"10.1109/ASAR.2017.8067763","DOIUrl":"https://doi.org/10.1109/ASAR.2017.8067763","url":null,"abstract":"Several inherent factors increase the complexity of automatic recognition of handwritten documents, such as the size of writing and the stroke width. In a previous work [1], we showed that a successful exploitation of the writing size improves the recognition performance. In this work we are interested in considering the stroke width as a factor in modeling, to improve the performance of automatic systems. The experiments were conducted on Arabic handwritten documents from one of the largest labeled Arabic handwriting databases, NIST-OpenHaRT. The database includes large variability in the stroke width. We propose several approaches to deal with these changes in both training and recognition phases. The first experiments show that the recognition is largely affected by the stroke width. To account for this parameter, we propose to classify data into three classes according to the stroke width. In the recognition phase, we have thickened each text-line image into several versions with predefined values, then we combined the recognition scores for each value. This approach has significant performance gains for both an HMM-based and a BLSTM-based recognition systems. In addition, we integrated synthetic data to adapt HMM models at different stroke width measures. We also obtained performance gains by two different combination methods (ROVER, trellis) on the adapted models results. We provide the obtained recognition results showing the benefits of exploiting the stroke width, and compare them with a known approach for stroke width normalization.","PeriodicalId":224611,"journal":{"name":"2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115972321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Text-line segmentation of large titles and headings in Arabic like script 文本行分割的大标题和标题在阿拉伯语类似的脚本

2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR) Pub Date : 2017-04-01 DOI: 10.1109/ASAR.2017.8067781

Riaz Ahmad, Muhammad Zeshan Afzal, Sheikh Faisal Rashid, M. Liwicki, A. Dengel

引用次数: 5