A Nom historical document recognition system for digital archiving

Phan, Truyen Van; Nguyen, Kha Cong; Nakagawa, Masaki

doi:10.1007/s10032-015-0257-8

Cited by 16 publications

(3 citation statements)

References 12 publications

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“…Automated text recognition plays a crucial role in processing paper documents 1 and detecting textual information in various image types, such as road signs and billboards in natural scenes 2 , 3 , handwritten notes 4 , and identifiers in diagrams 5 . Among the various text recognition tasks, this study focuses on automated hand-marked semantic text recognition (HMSTR) from photographs.…”

Section: Introductionmentioning

confidence: 99%

Automated hand-marked semantic text recognition from photographs

Suh,

Lee,

Gil

et al. 2023

Sci Rep

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Automated text recognition techniques have made significant advancements; however, certain tasks still present challenges. This study is motivated by the need to automatically recognize hand-marked text on construction defect tags among millions of photographs. To address this challenge, we investigated three methods for automating hand-marked semantic text recognition (HMSTR)—a modified scene text recognition-based (STR) approach, a two-step HMSTR approach, and a lumped approach. The STR approach involves locating marked text using an object detection model and recognizing it using a competition-winning STR model. Similarly, the two-step HMSTR approach first localizes the marked text and then recognizes the semantic text using an image classification model. By contrast, the lumped approach performs both localization and identification of marked semantic text in a single step using object detection. Among these approaches, the two-step HMSTR approach achieved the highest F1 score (0.92) for recognizing circled text, followed by the STR approach (0.87) and the lumped approach (0.78). To validate the generalizability of the two-step HMSTR approach, subsequent experiments were conducted using check-marked text, resulting in an F1 score of 0.88. Although the proposed methods have been tested specifically with tags, they can be extended to recognize marked text in reports or books.

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Section: Introductionmentioning

confidence: 99%

Automated hand-marked semantic text recognition from photographs

Suh,

Lee,

Gil

et al. 2023

Sci Rep

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“…Modern document entry systems allow to automatize the process of data extraction from various documents, either business, regulatory, or personal. Such systems are used for creating digital archives of historical documents [1], recognition of small-scale documents such as business cards [2], ID documents, driving licences, passports [3], as well as large-scale business documents [4].…”

Section: Introductionmentioning

confidence: 99%

Next integrated result modelling for stopping the text field recognition process in a video using a result model with per-character alternatives

Bulatov

Savelyev

Arlazarov

2020

Twelfth International Conference on Machine Vision (ICMV 2019)

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In the field of document analysis and recognition using mobile devices for capturing, and the field of object recognition in a video stream, an important problem is determining the time when the capturing process should be stopped. Efficient stopping influences not only the total time spent for performing recognition and data entry, but the expected accuracy of the result as well. This paper is directed on extending the stopping method based on next integrated recognition result modelling, in order for it to be used within a string result recognition model with per-character alternatives. The stopping method and notes on its extension are described, and experimental evaluation is performed on an open dataset MIDV-500. The method was compares with previously published methods based on input observations clustering. The obtained results indicate that the stopping method based on the next integrated result modelling allows to achieve higher accuracy, even when compared with the best achievable configuration of the competing methods.

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“…Given an input character image, the system provides functions to restore the image and presents similar character images already decoded, using simple pattern matching since the purpose is to nominate candidates and sample patterns are very limited. Truyen et al [11] developed a system for digitizing hundreds of thousands of Nom historical documents. Nom is the old Vietnamese writing system composed of original Chinese characters and Vietnamese characters created in the same way as Chinese characters, i.e., formed from radicals.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Anomalously Deformed Kana Sequences in Japanese Historical Documents

Nguyen

et al. 2019

IEICE Trans. Inf. & Syst.

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This paper presents recognition of anomalously deformed Kana sequences in Japanese historical documents, for which a contest was held by IEICE PRMU 2017. The contest was divided into three levels in accordance with the number of characters to be recognized: level 1: single characters, level 2: sequences of three vertically written Kana characters, and level 3: unrestricted sets of characters composed of three or more characters possibly in multiple lines. This paper focuses on the methods for levels 2 and 3 that won the contest. We basically follow the segmentationfree approach and employ the hierarchy of a Convolutional Neural Network (CNN) for feature extraction, Bidirectional Long Short-Term Memory (BLSTM) for frame prediction, and Connectionist Temporal Classification (CTC) for text recognition, which is named a Deep Convolutional Recurrent Network (DCRN). We compare the pretrained CNN approach and the end-to-end approach with more detailed variations for level 2. Then, we propose a method of vertical text line segmentation and multiple line concatenation before applying DCRN for level 3. We also examine a twodimensional BLSTM (2DBLSTM) based method for level 3. We present the evaluation of the best methods by cross validation. We achieved an accuracy of 89.10% for the three-Kana-character sequence recognition and an accuracy of 87.70% for the unrestricted Kana recognition without employing linguistic context. These results prove the performances of the proposed models on the level 2 and 3 tasks.

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A Nom historical document recognition system for digital archiving

Cited by 16 publications

References 12 publications

Automated hand-marked semantic text recognition from photographs

Automated hand-marked semantic text recognition from photographs

Next integrated result modelling for stopping the text field recognition process in a video using a result model with per-character alternatives

Recognition of Anomalously Deformed Kana Sequences in Japanese Historical Documents

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