Handwritten Word Verification by SVM-Based Hypotheses Re-scoring and Multiple Thresholds Rejection

Guichard, Laurent; Toselli, Alejandro H.; Coüasnon, Bertrand

doi:10.1109/icfhr.2010.15

Cited by 10 publications

(8 citation statements)

References 13 publications

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“…Every mail contains two to three pages, including the letter written by the volunteer, a form of the letter information, and an optimal fax sheet. Then the pages were scanned and the database was published to support testing of the tasks such as mail classification [30], handwritten recognition [31], and writer recognition [32].…”

Section: Existing Datasetsmentioning

confidence: 99%

KOHTD: Kazakh Offline Handwritten Text Dataset

Toiganbayeva,

Kasem,

Abdimanap

et al. 2021

Preprint

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Despite the transition to digital information exchange, many documents, such as invoices, taxes, memos and questionnaires, historical data, and answers to exam questions, still require handwritten inputs. In this regard, there is a need to implement Handwritten Text Recognition (HTR) which is an automatic way to decrypt records using a computer. Handwriting recognition is challenging because of the virtually infinite number of ways a person can write the same message. For this proposal we introduce Kazakh handwritten text recognition research, a comprehensive dataset of Kazakh handwritten texts is necessary. This is particularly true given the lack of a dataset for handwritten Kazakh text. In this paper, we proposed our extensive Kazakh offline Handwritten Text dataset (KOHTD), which has 3000 handwritten exam papers and more than 140335 segmented images and there are approximately 922010 symbols. It can serve researchers in the field of handwriting recognition tasks by using deep and machine learning. We used a variety of popular text recognition methods for word and line recognition in our studies, including CTCbased and attention-based methods. The findings demonstrate KOHTD's diversity. Also, we proposed a Genetic Algorithm (GA) for line and word segmentation based on random enumeration of a parameter. The dataset and GA code are available at https://github.com/abdoelsayed2016/KOHTD.

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Section: Existing Datasetsmentioning

confidence: 99%

KOHTD: Kazakh Offline Handwritten Text Dataset

Toiganbayeva,

Kasem,

Abdimanap

et al. 2021

Preprint

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show abstract

“…Each mail contains two to three pages, including the letter written by the contributor, a form with information about the letter, and an optional fax sheet. The pages were scanned, and the complete database was annotated to support evaluation of tasks like document layout analysis [28], mail classification [29], handwriting recognition [30] and writer recognition [17]. The National Institute of Standards and Technology, NIST, developed a series of databases [31] of handwritten characters and digits supporting tasks like isolation of fields, detection and removal of boxes in forms, character segmentation, and recognition.The form comprises boxes containing writer information, 28 boxes for numbers and 2 for alphabets, and 1 box for a paragraph of text.…”

Section: Related Workmentioning

confidence: 99%

HKR For Handwritten Kazakh & Russian Database

Nurseitov,

Bostanbekov,

Kurmankhojayev

et al. 2020

Preprint

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In this paper, we present a new Russian and Kazakh database (with about 95% of Russian and 5% of Kazakh words/sentences respectively) for offline handwriting recognition. A few pre-processing and segmentation procedures have been developed together with the database. The database is written in Cyrillic and shares the same 33 characters. Besides these characters, the Kazakh alphabet also contains 9 additional specific characters. This dataset is a collection of forms. The sources of all the forms in the datasets were generated by L A T E Xwhich subsequently was filled out by persons with their handwriting. The database consists of more than 1400 filled forms. There are approximately 63000 sentences, more than 715699 symbols produced by approximately 200 different writers. It can serve re-This work was done with the support of grant funding for scientific projects of the MES RK No AR05135175 Development and implementation of a system for recognizing handwritten addresses of written correspondence JSC KazPost using machine learning.

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“…IRISA has proposed two systems based on Continuous Densities HMMs [8]. They are composed of four modules: pre-processing, feature extraction, recognition and verification.…”

Section: Irisamentioning

confidence: 99%

ICDAR 2011 - French Handwriting Recognition Competition

Grosicki¹,

Abed

2011

2011 International Conference on Document Analysis and Recognition

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This paper describes the French handwriting recognition competition held at ICDAR 2011. This competition is based on the RIMES-database composed of French written documents corresponding to letters sent by individuals to companies or administrations. Two tasks have been proposed this year : the first one consists in recognizing isolated snippets of words with the help of a given dictionary; the second one consists in recognizing blocks of words segmented into lines. This year 9 systems were submitted for the different competition subtasks. A comparison between different classification and recognition systems show interesting results.A short description of the participating groups, their systems, and the results achieved are presented.

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Handwritten Word Verification by SVM-Based Hypotheses Re-scoring and Multiple Thresholds Rejection

Cited by 10 publications

References 13 publications

KOHTD: Kazakh Offline Handwritten Text Dataset

KOHTD: Kazakh Offline Handwritten Text Dataset

HKR For Handwritten Kazakh & Russian Database

ICDAR 2011 - French Handwriting Recognition Competition

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