Continuous Handwritten Script Recognition

Frinken, Volkmar; Bunke, Horst

doi:10.1007/978-0-85729-859-1_12

Cited by 19 publications

(6 citation statements)

References 49 publications

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“…Our network was trained from scratch because there is no pre-training model or transfer from another dataset used before. The standard performance measures are used for all the results presented: the character error rate (

) and word error rate (

) [ 47 ]. The

is determined as the distance from Levenshtein, which is the sum of the character substitution ( S ), insertion ( I ), and deletions ( D ) required to turn one string into another, divided by the total number of characters in the ground-truth word ( N ).…”

Section: Resultsmentioning

confidence: 99%

Attention-Based Fully Gated CNN-BGRU for Russian Handwritten Text

2020

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This article considers the task of handwritten text recognition using attention-based encoder–decoder networks trained in the Kazakh and Russian languages. We have developed a novel deep neural network model based on a fully gated CNN, supported by multiple bidirectional gated recurrent unit (BGRU) and attention mechanisms to manipulate sophisticated features that achieve 0.045 Character Error Rate (CER), 0.192 Word Error Rate (WER), and 0.253 Sequence Error Rate (SER) for the first test dataset and 0.064 CER, 0.24 WER and 0.361 SER for the second test dataset. Our proposed model is the first work to handle handwriting recognition models in Kazakh and Russian languages. Our results confirm the importance of our proposed Attention-Gated-CNN-BGRU approach for training handwriting text recognition and indicate that it can lead to statistically significant improvements (p-value < 0.05) in the sensitivity (recall) over the tests dataset. The proposed method’s performance was evaluated using handwritten text databases of three languages: English, Russian, and Kazakh. It demonstrates better results on the Handwritten Kazakh and Russian (HKR) dataset than the other well-known models.

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) and word error rate (

) [ 47 ]. The

Section: Resultsmentioning

confidence: 99%

Attention-Based Fully Gated CNN-BGRU for Russian Handwritten Text

2020

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“…We used two ways to evaluate models in this article: For the results reported in the first technique, established performance measurements such as the character error rate (CER) and word error rate (WER) [52] are employed. The Levenshtein distance is calculated by dividing the total number…”

Section: Evaluation Methodsmentioning

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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“…The Character Error Rate (CER) and the Word Error Rate (WER) [48] are the performance measures. The CER is computed as the Levenshtein distance, which is the sum of the character substitutions (S c ), insertions (I c ) and deletions (D c ) that are needed to transform one string into the other, divided by the total number of characters in the groundtruth (N c ).…”

Section: Datasets and Metricsmentioning

confidence: 99%

Content and Style Aware Generation of Text-Line Images for Handwriting Recognition

Kang

Riba²,

Rusiñol³

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

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Handwritten Text Recognition has achieved an impressive performance in public benchmarks. However, due to the high inter-and intra-class variability between handwriting styles, such recognizers need to be trained using huge volumes of manually labeled training data. To alleviate this labor-consuming problem, synthetic data produced with TrueType fonts has been often used in the training loop to gain volume and augment the handwriting style variability. However, there is a significant style bias between synthetic and real data which hinders the improvement of recognition performance. To deal with such limitations, we propose a generative method for handwritten text-line images, which is conditioned on both visual appearance and textual content. Our method is able to produce long text-line samples with diverse handwriting styles. Once properly trained, our method can also be adapted to new target data by only accessing unlabeled text-line images to mimic handwritten styles and produce images with any textual content. Extensive experiments have been done on making use of the generated samples to boost Handwritten Text Recognition performance. Both qualitative and quantitative results demonstrate that the proposed approach outperforms the current state of the art.

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Continuous Handwritten Script Recognition

Cited by 19 publications

References 49 publications

Attention-Based Fully Gated CNN-BGRU for Russian Handwritten Text

Attention-Based Fully Gated CNN-BGRU for Russian Handwritten Text

KOHTD: Kazakh Offline Handwritten Text Dataset

Content and Style Aware Generation of Text-Line Images for Handwriting Recognition

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