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2019 | OriginalPaper | Buchkapitel

A Study on the Effect of CNN-Based Transfer Learning on Handwritten Indic and Mixed Numeral Recognition

verfasst von : Rahul Pramanik, Prabhat Dansena, Soumen Bag

Erschienen in: Document Analysis and Recognition

Verlag: Springer Singapore

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Abstract

Filling up forms at post offices, railway counters, and for application of jobs has become a routine for modern people, especially in a developing country like India. Research on automation for the recognition of such handwritten forms has become mandatory. This applies more for a multilingual country like India. In the present work, we use readily available pre-trained Convolutional Neural Network (CNN) architectures on four different Indic scripts, viz. Bangla, Devanagari, Oriya, and Telugu to achieve a satisfactory recognition rate for handwritten Indic numerals. Furthermore, we have mixed Bangla and Oriya numerals and applied transfer learning for recognition. The main objective of this study is to realize how good a CNN model trained on an entire different dataset (of natural images) works for small and unrelated datasets. As a part of practical application, we have applied the proposed approach to recognize Bangla handwritten pin codes after their extraction from postal letters.

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Vorheriges Kapitel Textual Content Retrieval from Filled-in Form Images

Nächstes Kapitel Online Handwritten Bangla Character Recognition Using Frechet Distance and Distance Based Features

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Titel: A Study on the Effect of CNN-Based Transfer Learning on Handwritten Indic and Mixed Numeral Recognition
verfasst von: Rahul Pramanik
Prabhat Dansena
Soumen Bag
Verlag: Springer Singapore
Buch: Document Analysis and Recognition
Print ISBN: 978-981-13-9360-0

Electronic ISBN: 978-981-13-9361-7

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-981-13-9361-7_4

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