# Classification of Handwritten Names of Cities and Handwritten Text Recognition using Various Deep Learning Models

arXiv: https://arxiv.org/abs/2102.04816 · HTML: https://mlsanity.com/paper/2102.04816/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 42 references that are in the map.
- First posted on arXiv 9 Feb 2021.

## Builds on (most cited first, 12 of 12)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Unsupervised Learning of Video Representations using LSTMs](https://mlsanity.com/paper/1502.04681/) (2015)
- [Deep Speech: Scaling up end-to-end speech recognition](https://mlsanity.com/paper/1412.5567/) (2014)
- [Attention on Attention for Image Captioning](https://mlsanity.com/paper/1908.06954/) (2019)
- [Attention-based Fully Gated CNN-BGRU for Russian Handwritten Text](https://mlsanity.com/paper/2008.05373/) (2020)
- [Enhancing Handwritten Text Recognition with N-gram sequence decomposition and Multitask Learning](https://mlsanity.com/paper/2012.14459/) (2020)
- [Fully Convolutional Networks for Handwriting Recognition](https://mlsanity.com/paper/1907.04888/) (2019)
- [Automated Question Answer medical model based on Deep Learning Technology](https://mlsanity.com/paper/2005.10416/) (2020)
- [Window-Based Descriptors for Arabic Handwritten Alphabet Recognition: A Comparative Study on a Novel Dataset](https://mlsanity.com/paper/1411.3519/) (2014)

## Built on by (most cited first, 5 of 5)

- [TNCR: Table Net Detection and Classification Dataset](https://mlsanity.com/paper/2106.15322/) (2021)
- [Advancements and Challenges in Arabic Optical Character Recognition: A Comprehensive Survey](https://mlsanity.com/paper/2312.11812/) (2023)
- [Transformers and Language Models in Form Understanding: A Comprehensive Review of Scanned Document Analysis](https://mlsanity.com/paper/2403.04080/) (2024)
- [Customer Profiling, Segmentation, and Sales Prediction using AI in Direct Marketing](https://mlsanity.com/paper/2302.01786/) (2023)
- [A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking](https://mlsanity.com/paper/2405.05900/) (2024)

## Abstract (arXiv)

This article discusses the problem of handwriting recognition in Kazakh and Russian languages. This area is poorly studied since in the literature there are almost no works in this direction. We have tried to describe various approaches and achievements of recent years in the development of handwritten recognition models in relation to Cyrillic graphics. The first model uses deep convolutional neural networks (CNNs) for feature extraction and a fully connected multilayer perceptron neural network (MLP) for word classification. The second model, called SimpleHTR, uses CNN and recurrent neural network (RNN) layers to extract information from images. We also proposed the Bluechet and Puchserver models to compare the results. Due to the lack of available open datasets in Russian and Kazakh languages, we carried out work to collect data that included handwritten names of countries and cities from 42 different Cyrillic words, written more than 500 times in different handwriting. We also used a handwritten database of Kazakh and Russian languages (HKR). This is a new database of Cyrillic words (not only countries and cities) for the Russian and Kazakh languages, created by the authors of this work.