SubWord Embeddings for OCR Corrections in Highly Fusional Indic Languages Texts in Indic Languages contain a large proportion of outofvocabulary (OOV) words due to frequent fusion using conjoining rules (of which there are around 4000 in Sanskrit). OCR errors further accentuate this complexity for the error correction systems. Variations of subword units such as ngrams, possibly encapsulating the context, can be extracted from the OCR text as well as the language text individually. Some of the subword units that are derived from the texts in such languages highly correlate to the word conjoining rules. Signals such as frequency values (on a corpus) associated with such subword units have been used previously with loglinear classifiers for detecting errors in Indic OCR texts. We explore two different encodings to capture such signals and augment the input to Long Short Term Memory (LSTM) based OCR correction models, that have proven useful in the past for jointly learning the language as well as OCRspecific confusions. The first type of encoding makes direct use of subword unit frequency values, derived from the training data. The formulation results in faster convergence and better accuracy values of the error correction model on four different languages with varying complexities. The second type of encoding makes use of trainable subword embeddings. We introduce a new procedure for training fastText embeddings on the subword units and further observe a large gain in FScores, as well as wordlevel accuracy values.

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.