Handwritten Arabic and Roman word recognition using holistic approach

Malakar, Samir; Sahoo, Samanway; Chakraborty, Anuran; Sarkar, Ram; Nasipuri, Mita

doi:10.1007/s00371-022-02500-7

Cited by 7 publications

(1 citation statement)

References 51 publications

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“…From the previous work, we found that segmentationbased word recognition assigns character confidence levels for the segments of words to account for the ambiguity within character classes [7]. Numerous design initiatives for character recognition are theoretically based on practically all classification techniques, including neural nets, linear discriminant functions, fuzzy logic [8], template matching [9], binary comparisons, etc [10]. It is equally crucial to choose the nominal method to use as it is to decide which attributes to analyze and how to measure them.…”

Section: Introductionmentioning

confidence: 99%

Handwritten Word Recognition using Deep Learning Approach: A Novel Way of Generating Handwritten Words

Akter

Shahriar

Cuzzocrea

et al. 2022

2022 IEEE International Conference on Big Data (Big Data)

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A handwritten word recognition system comes with issues such as-lack of large and diverse datasets. It is necessary to resolve such issues since millions of official documents can be digitized by training deep learning models using a large and diverse dataset. Due to the lack of data availability, the trained model does not give the expected result. Thus, it has a high chance of showing poor results. This paper proposes a novel way of generating diverse handwritten word images using handwritten characters. The idea of our project is to train the BiLSTM-CTC architecture with generated synthetic handwritten words. The whole approach shows the process of generating two types of large and diverse handwritten word datasets: overlapped and non-overlapped. Since handwritten words also have issues like overlapping between two characters, we have tried to put it into our experimental part. We have also demonstrated the process of recognizing handwritten documents using the deep learning model. For the experiments, we have targeted the Bangla language, which lacks the handwritten word dataset, and can be followed for any language. Our approach is less complex and less costly than traditional GAN models. Finally, we have evaluated our model using Word Error Rate (WER), accuracy, f1-score, precision, and recall metrics. The model gives 39% WER score, 92% percent accuracy, and 92% percent f1 scores using nonoverlapped data and 63% percent WER score, 83% percent accuracy, and 85% percent f1 scores using overlapped data.

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