SubWord Based Mongolian Offline Handwriting Recognition Mongolian is an agglutinative language, which results in a large number of words derived from the same stems connecting different suffixes. This morphological richness leads to high outofvocabulary (OOV) rates and causes problems of data sparsity. In this paper, our proposed recognition system is composed of three parts: handwritten image preprocessing, mapping of images to grapheme sequences, and subwordbased language model(LM) decoding. We present a subwordbased ngram LM to solve the high OOV rate problem. According to the characteristics of Mongolian, we modified the traditional token passing algorithm to improve decoding speed and to easy to combine with any ngram LM. We evaluated the performance of subwords at different levels on the open Mongolian offline handwriting dataset(MHW). The bisyllable 2gram LM showed the best performance, with 18.32% and 23.22% worderror rates (WERs) on two test sets. Our various experiments show that, this method can predict in vocabulary words with a higher accuracy rate and also predict OOV words with a certain accuracy rate.

Using ASR Methods for OCR Hybrid deep neural network hidden Markov models (DNNHMM) have achieved impressive results on large vocabulary continuous speech recognition (LVCSR) tasks. However, the recent approaches using DNNHMM models are not explored much for text recognition. Inspired by the current work in automatic speech recognition (ASR) and machine translation, we present an open vocabulary subword text recognition system. The subword lexicon and subword language model (LM) helps in overcoming the challenge of recognizing out of vocabulary (OOV) words, and a time delay neural network (TDNN) and convolution neural network (CNN) based DNNHMM optical model (OM) efficiently models the sequence dependency in the line image. We present results on 12 datasets with training data varying from 6k lines to 600k lines. The system is built for 8 languages, i.e., English, French, Arabic, Chinese, Farsi, Tamil, Russian, and Korean. We report competitive results on several commonly used handwritten and printed text datasets.