{"title":"A Study of Handwritten Characters by Shape Descriptors: Doping Using the Freeman Code","authors":"C. Gmati, Sofiene Haboubi, A. Alaqeeli, H. Amiri","doi":"10.1109/ICFHR.2012.170","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.170","url":null,"abstract":"In this paper, we present the role of shape descriptors in the off-line recognition process of handwritten isolated Arabic and Latin characters. We will give some statistical and structural shape descriptors and mention their performance. Then we will present an hybrid approach that uses structural shapes' descriptors from a different angle in order to improve the recognition's results from a statistical descriptor. We will therefore introduce the concept of doping aiming to raise the recognition rate.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114329175","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}
{"title":"Recovering Dynamic Stroke Information of Multi-stroke Handwritten Characters with Complex Patterns","authors":"Takayuki Nagoya, H. Fujioka","doi":"10.1109/ICFHR.2012.258","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.258","url":null,"abstract":"In this paper, we consider the problem of recovering dynamic stroke information from multi-stroke handwritten character images with complex patterns. The characters are assumed to be of a class of characters whose strokes are recursively formulated. By employing the so-called graph theoretic approach, we develop a systematic algorithm for recovering dynamic stroke information from character images in the class of our interest. It is shown that the correctness of algorithm is guaranteed mathematically. Moreover, we show that the time complexity becomes O(n), where n denotes the number of stroke-intersections on characters. Some recovery examples are included.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115378807","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}
Anis Mezghani, S. Kanoun, Maher Khemakhem, H. E. Abed
{"title":"A Database for Arabic Handwritten Text Image Recognition and Writer Identification","authors":"Anis Mezghani, S. Kanoun, Maher Khemakhem, H. E. Abed","doi":"10.1109/ICFHR.2012.155","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.155","url":null,"abstract":"Standard databases play essential roles for evaluating and comparing results obtained by different groups of researchers. In this paper, an Arabic Handwritten Text Images Database written by Multiple Writers (AHTID/MW) is introduced. This database can be used for research in the recognition of Arabic handwritten text with open vocabulary, word segmentation and writer identification. The AHTID/MW contains 3710 text lines and 22896 words written by 53 native writers of Arabic. In addition, ground truth annotation is provided for each text image. The database is freely available for worldwide researchers.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115857145","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}
{"title":"Multi-lingual City Name Recognition for Indian Postal Automation","authors":"U. Pal, Rami Kumar Roy, F. Kimura","doi":"10.1109/ICFHR.2012.238","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.238","url":null,"abstract":"Under three-language formula, the destination address block of postal document of an Indian state is generally written in three languages: English, Hindi and the State official language. From the statistical analysis we found that 12.37%, 76.32% and 10.21% postal documents are written in Bangla, English and Devanagari script, respectively. Because of inter-mixing of these scripts in postal address writings, it is very difficult to identify the script by which a city name is written. To avoid such script identification difficulties, in this paper we proposed a lexicon-driven method for multi-lingual (English, Hindi and Bangla) city name recognition for Indian postal automation. In the proposed scheme, at first, to take care of slanted handwriting of different individuals a slant correction technique is performed. Next, a water reservoir concept is applied to pre-segment the slant corrected city names into possible primitive components (characters or its parts). Pre-segmented components of a city name are then merged into possible characters to get the best city name using the lexicon information. In order to merge these primitive components into characters and to find optimum character segmentation, dynamic programming (DP) is applied using total likelihood of the characters of a city name as an objective function. We tested our system on 16132 Indian trilingual city names and 92.25% overall recognition accuracy was obtained.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116545765","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}
Ines Ben Messaoud, H. Amiri, H. E. Abed, V. Märgner
{"title":"Region Based Local Binarization Approach for Handwritten Ancient Documents","authors":"Ines Ben Messaoud, H. Amiri, H. E. Abed, V. Märgner","doi":"10.1109/ICFHR.2012.261","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.261","url":null,"abstract":"Due to the fact that historical handwritten documents present many degradations, pre-processing of such documents is considered as a big challenge. Most pre-processing methods and specifically binarization return better results when they are applied on printed documents. We present in this paper a binarization approach adaptive for handwritten historical documents based on extraction of regions-of-interest. During our tests several images datasets are used, the benchmarking datasets for binarization DIBCO 2009 and H-DIBCO 2010 (15 images) as well as complete handwritten documents from the IAM historical database (about 60 images). The evaluation of the proposed binarization method is based on several evaluation metrics for binarization. The results show that the proposed method fit with handwritten historical documents (FM about 88%) for images of the binarization competitions.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"2130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129973682","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}
S. S. Bukhari, T. Breuel, Abedelkadir Asi, Jihad El-Sana
{"title":"Layout Analysis for Arabic Historical Document Images Using Machine Learning","authors":"S. S. Bukhari, T. Breuel, Abedelkadir Asi, Jihad El-Sana","doi":"10.1109/ICFHR.2012.227","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.227","url":null,"abstract":"Page layout analysis is a fundamental step of any document image understanding system. We introduce an approach that segments text appearing in page margins (a.k.a side-notes text) from manuscripts with complex layout format. Simple and discriminative features are extracted in a connected-component level and subsequently robust feature vectors are generated. Multilayer perception classifier is exploited to classify connected components to the relevant class of text. A voting scheme is then applied to refine the resulting segmentation and produce the final classification. In contrast to state-of-the-art segmentation approaches, this method is independent of block segmentation, as well as pixel level analysis. The proposed method has been trained and tested on a dataset that contains a variety of complex side-notes layout formats, achieving a segmentation accuracy of about 95%.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128245611","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}
{"title":"An MLP for Binarizing Images of Old Manuscripts","authors":"T. Sari, Abderhmane Kefali, Halima Bahi","doi":"10.1109/ICFHR.2012.176","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.176","url":null,"abstract":"Ancient Arabic manuscripts' processing and analysis are very difficult tasks and are likely to remain open problems for many years to come. In this paper we tackle the problem of foreground/background separation in old documents. Our approach uses a back-propagation neural network to directly classify image pixels according to their neighborhood. We tried several multilayer Perceptron topologies and found experimentally the optimal one. Experiments were run on synthetic data obtained by image fusion techniques. The results are very promising compared to state-of-the-art techniques.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128772704","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}
{"title":"Segmentation and Recognition Strategy of Handwritten Connected Digits Based on the Oriented Sliding Window","authors":"A. Gattal, Y. Chibani","doi":"10.1109/ICFHR.2012.265","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.265","url":null,"abstract":"In this paper, we propose a system to recognize handwritten digit strings, which constitutes a difficult task because of overlapping and/or joining of adjacent digits. To resolve this problem, we use a segmentation-recognition of handwritten connected digits based on the oriented sliding window. The proposed approach allows separating adjacent digits according the connection configuration by finding at the same time the interconnection points between adjacent digits and the cutting path. The segmentation-recognition using the global decision module allows the rejection or acceptance of the processed image. Experimental results conducted on the handwritten digit database NIST SD19 show the effective use of the sliding window for segmentation-recognition.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130055138","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}
{"title":"A Novel Naive Bayes Voting Strategy for Combining Classifiers","authors":"C. Stefano, F. Fontanella, A. S. D. Freca","doi":"10.1109/ICFHR.2012.166","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.166","url":null,"abstract":"Classifier combination methods have proved to be an effective tool for increasing the performance in pattern recognition applications. The rationale of this approach follows from the observation that appropriately diverse classifiers make uncorrelated errors. Unfortunately, this theoretical assumption is not easy to satisfy in practical cases, thus reducing the performance obtainable with any combination strategy. In this paper we propose a new weighted majority vote rule which try to solve this problem by jointly analyzing the responses provided by all the experts, in order to capture their collective behavior when classifying a sample. Our rule associates a weight to each class rather than to each expert and computes such weights by estimating the joint probability distribution of each class with the set of responses provided by all the experts in the combining pool. The probability distribution has been computed by using the naive Bayes probabilistic model. Despite its simplicity, this model has been successfully used in many practical applications, often competing with much more sophisticated techniques. The experimental results, performed by using three standard databases of handwritten digits, confirmed the effectiveness of the proposed method.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132741961","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}
{"title":"Statistical Text Line Analysis in Handwritten Documents","authors":"Vicente Bosch, A. Rossi, E. Vidal","doi":"10.1109/ICFHR.2012.274","DOIUrl":"https://doi.org/10.1109/ICFHR.2012.274","url":null,"abstract":"In this paper we present an approach for text line analysis and detection in handwritten documents based on Hidden Markov Models, a technique widely used in other handwritten and speech recognition tasks. It is shown that text line analysis and detection can be solved using a more formal methodology in contraposition to most of the proposed heuristic approaches found in the literature. Our approach not only provides the best position coordinates for each of the vertical page regions but also labels them, in this manner surpassing the traditional heuristic methods. In our experiments we demonstrate the performance of the approach (both in line analysis and detection) and study the impact of increasingly constrained \"vertical layout language models\" on text line detection accuracy. Through this experimentation we also show the improvement in quality of the baselines yielded by our approach in comparison with a state-of-the-art heuristic method based on vertical projection profiles.","PeriodicalId":291062,"journal":{"name":"2012 International Conference on Frontiers in Handwriting Recognition","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132330883","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}