A New Approach for Integrated Recognition and Correction of Texts from Images Automatic recognition and error correction of texts from images are critical for many commercial applications such as receipt recognition, which have very high accuracy requirements. In this paper we propose an integrated image based text recognition and correction approach to improve accuracy. There are two levels of text recognition and correction integration in the proposed approach. Firstly, a beam search strategy is designed to generate a set of text candidates, based on the probability distribution of text prediction outcomes from a deep learning recognition model. Then a wordlevel lexicon check is applied to select only one from the candidate text sentences, which has the highest prediction probability among those with all words present in the lexicon. Jointly the beam search and lexicon check can effectively correct some recognition errors. Secondly, an encoderdecoder language model based corrector is developed to correct potential recognition errors in the selected output texts that fail the lexicon check. Training samples for the corrector are created from the recognition outcomes and can be expanded by associating multiple text candidates with one image label. We conduct experiments on ICDARu002713 and CH10K datasets to evaluate the proposed approach and the impact of these two levels of integration on accuracy. Experiment results show that the proposed approach outperforms the existing one with higher recall and much higher recognition accuracy through effective exploitation of joint recognition and correction design.

A Cost Efficient Approach to Correct OCR Errors in Large Document Collections Word error rate of an OCR is often higher than its character error rate. This is especially true when OCRs are designed by recognizing characters. High word accuracies are critical for many practical applications like content creation and texttospeech systems. In order to detect and correct the misrecognised words, it is common for an OCR to employ a postprocessor module to improve the word accuracy. However, conventional approaches to postprocessing like looking up a dictionary or using a statistical language model (SLM), are still limited. In many such scenarios, it is often required to remove the outstanding errors manually. We observe that the traditional postprocessing schemes look at error words sequentially, since OCRs process documents one at a time. We propose a costefficient model to address the error words in batches rather than correcting them individually. We exploit the fact that a collection of documents (eg. a book), unlike a single document, has a structure leading to repetition of words. Such words, if efficiently grouped together and corrected together, can lead to a significant reduction in the effort. Error correction can be fully automatic or with a human in the loop. We compare the performance of our method with various baseline approaches including the case where all the errors are removed by a human. We demonstrate the efficacy of our solution empirically by reporting more than 70% reduction in the human effort with near perfect error correction. We validate our method on books in both English and Hind