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Meitei Mayek handwritten dataset: compilation, segmentation, and character recognition

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Abstract

A peculiar Indian Script Meitei Mayek has experienced a resurgence in the last few years and gets very little attention in handwriting research due to recently insurgence and limited sources. The objective of this paper is two folds; firstly, develop two different datasets: Mayek27 having 4900 isolated Meitei Mayek alphabets and MM (Meitei Mayek) dataset of 189 full-length handwritten text page. Secondly, develop a recognition system on the Mayek27 dataset using convolutional neural network and segmentation algorithms (text-lines, words, and characters) on the full-length Meitei Mayek handwritten text. A recognition rate of \(99.02\%\) is achieved using three layers of convolutional layers with a filter size of \(3 \times 3\) with 16, 32, and 96 kernels. In MM text dataset, the text-line and word segmentation are performed concurrently on 809 lines by tracking space between lines in a novel approach based on horizontal projection histogram and monitoring vertical projection histogram along the run-length of segmentation. Various constraints like skew, curve, close, and touching text-lines are incorporated, and the segmentation algorithm results are 91.84% and 88.96% for text-line and word, respectively. Furthermore, characters are segmented by headline removal, and connected component analysis achieves an accuracy of 91.12%.

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  1. National Institute of Technology, Langol, Imphal, Manipur, 795004, India

    Sanasam Inunganbi & Khumanthem Manglem

  2. National Institute of Technology, Hamirpur, Himachal Pradesh, India

    Prakash Choudhary

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  1. Sanasam Inunganbi
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Correspondence to Sanasam Inunganbi.

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All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for relevant intellectual content; and (c) approval of the final version. The stated authors have written the article, and the work is original which is not published elsewhere. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript. The below authors have affiliations with organizations with a direct or indirect financial interest in the subject matter discussed in the manuscript: Sanasam Inunganbi (National Institute of Technology, Manipur) Prakash Choudhary (National Institute of Technology, Hamirpur) Khumanthem Manglem Singh (National Institute of Technology, Manipur)

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Inunganbi, S., Choudhary, P. & Manglem, K. Meitei Mayek handwritten dataset: compilation, segmentation, and character recognition. Vis Comput 37, 291–305 (2021). https://doi.org/10.1007/s00371-020-01799-4

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