DAR '12 Pub Date : 2012-12-16 DOI: 10.1145/2432553.2432559

Rajneesh Rani, R. Dhir, Gurpreet Singh Lehal

引用次数: 7

Super-resolution of single text image by sparse representation 基于稀疏表示的单幅文本图像的超分辨率

DAR '12 Pub Date : 2012-12-16 DOI: 10.1145/2432553.2432558

Rim Walha, Fadoua Drira, Frank Lebourgeois, A. Alimi

引用次数: 19

Detection and removal of hand-drawn underlines in a document image using approximate digital straightness 使用近似数字直线度检测和去除文档图像中的手绘下划线

DAR '12 Pub Date : 2012-12-16 DOI: 10.1145/2432553.2432576

Sanjoy Pratihar, Partha Bhowmick, S. Sural, J. Mukhopadhyay

引用次数: 2

Margin noise removal from printed document images 从打印文档图像中去除边缘噪声

DAR '12 Pub Date : 2012-12-16 DOI: 10.1145/2432553.2432570

Soumyadeep Dey, J. Mukhopadhyay, S. Sural, Partha Bhowmick

引用次数: 10

On performance analysis of end-to-end OCR systems of Indic scripts 端到端印度文字OCR系统的性能分析

DAR '12 Pub Date : 2012-12-16 DOI: 10.1145/2432553.2432577

P. P. Kumar, C. Bhagvati, A. Agarwal

引用次数: 4

Hindi handwritten word recognition using HMM and symbol tree 使用HMM和符号树的印地语手写单词识别

DAR '12 Pub Date : 2012-12-16 DOI: 10.1145/2432553.2432556

S. Belhe, Chetan Paulzagade, Akash Deshmukh, Saumya Jetley, Kapil Mehrotra

{"title":"Hindi handwritten word recognition using HMM and symbol tree","authors":"S. Belhe, Chetan Paulzagade, Akash Deshmukh, Saumya Jetley, Kapil Mehrotra","doi":"10.1145/2432553.2432556","DOIUrl":"https://doi.org/10.1145/2432553.2432556","url":null,"abstract":"The proposed approach performs recognition of online handwritten isolated Hindi words using a combination of HMMs trained on Devanagari symbols and a tree formed by the multiple, possible sequences of recognized symbols.\u0000 In general, words in Indic languages are composed of a number of aksharas or syllables, which in turn are formed by groups of consonants and vowel modifiers. Segmentation of aksharas is critical to accurate recognition of both recognition primitives as well as the complete word. Also, recognition in itself is an intricate job. This holistic task of akshara segmentation, symbol identification and subsequent word recognition is targeted in our work. It is handled in an integrated segmentation-recognition framework. By making use of online stroke information for postulating symbol candidates and deriving HOG feature set from their image counterparts, the recognition becomes independent of stroke order and stroke shape variations. Thus, the system is well suited to unconstrained handwriting.\u0000 Data for this work is collected from different parts of India where Hindi language is predominantly in use. Symbols extracted from 60,000 words are used to train and test 140 symbol-HMM models. The system is designed to output one or more candidate words to the user, by tracing multiple tree paths (up to leaf nodes) under the condition that the symbol likelihood (confidence score) at every node is above threshold. Tests performed on 10,000 words yield an accuracy of 89%.","PeriodicalId":410986,"journal":{"name":"DAR '12","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131049408","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: 24

Automatic localization and correction of line segmentation errors 自动定位和修正线段错误

DAR '12 Pub Date : 2012-12-16 DOI: 10.1145/2432553.2432555

Anand Mishra, Naveen Sankaran, Viresh Ranjan, C. V. Jawahar

引用次数: 1

Lightweight user-adaptive handwriting recognizer for resource constrained handheld devices 轻量级用户自适应手写识别器，用于资源受限的手持设备

DAR '12 Pub Date : 2012-12-16 DOI: 10.1145/2432553.2432574

D. Dutta, Aruni Roy Chowdhury, U. Bhattacharya, S. K. Parui

{"title":"Lightweight user-adaptive handwriting recognizer for resource constrained handheld devices","authors":"D. Dutta, Aruni Roy Chowdhury, U. Bhattacharya, S. K. Parui","doi":"10.1145/2432553.2432574","DOIUrl":"https://doi.org/10.1145/2432553.2432574","url":null,"abstract":"Here, we present our recent attempt to develop a lightweight handwriting recognizer suitable for resource constrained handheld devices. Such an application requires real-time recognition of handwritten characters produced on their touchscreens. The proposed approach is well suited for minimal user-lag on devices having only limited computing power in sharp contrast to standard laptops or desktop computers. Moreover, the approach is user-adaptive in the sense that it can adapt through user corrections to wrong predictions. With an increasing number of interactive corrections by the user, the recognition accuracy improves significantly. An input stroke is first re-sampled generating a fixed small number of sample points such that at most two critical points (points corresponding to high curvature) are preserved. We use their x- and y-coordinates as the feature vector and do not compute any other high-level feature vector. The squared Mahalanobis distance is used to identify each stroke of the input sample as one of several stroke categories pre-determined based on a large pool of training samples. The inverted covariance matrix and mean vector for a stroke class that are required for computing the Mahalanobis distance are pre-calculated and stored as Serialized Objects on the SD card of the device. A Look-Up Table (LUT) of stroke combinations as keys and corresponding character class as values is used for the final Unicode character output. In case of an incorrect character output, user corrections are used to automatically update the LUT adapting to the user's particular handwriting style.","PeriodicalId":410986,"journal":{"name":"DAR '12","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132354328","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: 4

Model based table cell detection and content extraction from degraded document images 基于模型的退化文档图像的表单元检测和内容提取

DAR '12 Pub Date : 2012-12-16 DOI: 10.1145/2432553.2432565

Zhixin Shi, S. Setlur, V. Govindaraju

{"title":"Model based table cell detection and content extraction from degraded document images","authors":"Zhixin Shi, S. Setlur, V. Govindaraju","doi":"10.1145/2432553.2432565","DOIUrl":"https://doi.org/10.1145/2432553.2432565","url":null,"abstract":"This paper describes a novel method for detection and extraction of contents of table cells from handwritten document images. Given a model of the table and a document image containing a table, the hand-drawn or pre-printed table is detected and the contents of the table cells are extracted automatically. The algorithms described are designed to handle degraded binary document images. The target images may include a wide variety of noise, ranging from clutter noise, salt-and-pepper noise to non-text objects such as graphics and logos.\u0000 The presented algorithm effectively eliminates extraneous noise and identifies potentially matching table layout candidates by detecting horizontal and vertical table line candidates. A table is represented as a matrix based on the locations of intersections of horizontal and vertical table lines, and a matching algorithm searches for the best table structure that matches the given layout model and using the matching score to eliminate spurious table line candidates. The optimally matched table candidate is then used for cell content extraction.\u0000 This method was tested on a set of document page images containing tables from the challenge set of the DARPA MADCAT Arabic handwritten document image data. Preliminary results indicate that the method is effective and is capable of reliably extracting text from the table cells.","PeriodicalId":410986,"journal":{"name":"DAR '12","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114165693","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: 4

Choice of recognizable units for URDU OCR 乌尔都OCR可识别单位的选择

DAR '12 Pub Date : 2012-12-16 DOI: 10.1145/2432553.2432569

Gurpreet Singh Lehal

{"title":"Choice of recognizable units for URDU OCR","authors":"Gurpreet Singh Lehal","doi":"10.1145/2432553.2432569","DOIUrl":"https://doi.org/10.1145/2432553.2432569","url":null,"abstract":"There has been considerable work on Arabic OCR. However, all that work is based on Naskh style. Urdu script is based on Arabic alphabet, but uses Nastalique style. The Nastalique style makes OCR in general and character segmentation in particular, a highly challenging task, so most of the researchers avoid the character segmentation phase and go in for higher unit of recognition. For Urdu, the next higher recognition unit considered by researchers is ligature, which lies between character and word. A ligature is a connected component of one or more characters and usually an Urdu word is composed of 1 to 8 ligatures. A related issue is identification of all possible ligatures for recognition purpose. For this purpose, we have performed a statistical analysis of Urdu corpus to collect and organise the Urdu ligatures. The number of unique ligatures comes to be more than 26,000, and recognition of such a huge class is again a Herculean task. It becomes necessary to reduce the class count and look for alternative recognition unit. From OCR point of view, a ligature can further be segmented into one primary connected component and zero or more secondary connected components. The primary component represents the basic shape of the ligature, while the secondary connected component corresponds to the dots and diacritics marks and special symbols associated with the ligature. To reduce the class count, the ligatures with similar primary components are clubbed together. Further statistical analysis is performed to count and arrange in descending order the primary components and a manageable class of around 2300 recognition units has been generated, which covers 99% of Urdu corpus.","PeriodicalId":410986,"journal":{"name":"DAR '12","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126956151","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: 30

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