Krishna Subramanian

Multi-lingual offline handwriting recognition using hidden Markov models: a script-independent approach

This paper introduces a script-independent methodology for multilingual offline handwriting recognition (OHR) based on the use of Hidden Markov Models (HMM). The OHR methodology extends our script-independent approach for OCR of machine-printed text images. The feature extraction, training, and recognition components of the system are all designed to be script independent. The HMM training and recognition components are based on our Byblos continuous speech recognition system. The HMM parameters are estimated automatically from the training data, without the need for laborious hand-written rules. The system does not require pre-segmentation of the data, neither at the word level nor at the character level. Thus, the system can handle languages with cursive handwritten scripts in a straightforward manner. The script independence of the system is demonstrated with experimental results in three scripts that exhibit significant differences in glyph characteristics: English, Chinese, and Arabic. Results from an initial set of experiments are presented to demonstrate the viability of the proposed methodology.

Improvements in BBN's HMM-Based Offline Arabic Handwriting Recognition System

Offline handwriting recognition of free-flowing Arabic text is a challenging task due to the plethora of factors that contribute to the variability in the data. In this paper, we address some of these sources of variability, and present experimental results on a large corpus of handwritten documents. Specific techniques such as the application of context-dependent Hidden Markov Models (HMMs) for the cursive Arabic script, unsupervised adaptation to account for the stylistic variations across scribes, and image pre-processing to remove ruled-lines are explored. In particular, we proposed a novel integration of structural features in the HMM framework which exclusively results in a 9% relative improvement in performance. Overall, we demonstrate a relative reduction of 17% in word error rate over our baseline Arabic handwriting recognition system.

Character-Stroke Detection for Text-Localization and Extraction

In this paper, we present a new approach for analysis of images for text-localization and extraction. Our approach puts very few constraints on the font, size and color of text and is capable of handling both scene text and artificial text well. In this paper, we exploit two well-known features of text: approximately constant stroke width and local contrast, and develop a fast, simple, and effective algorithm to detect character strokes. We also show how these can be used for accurate extraction and motivate some advantages of using this approach for text localization over other color-space segmentation based approaches. We analyze the performance of our stroke detection algorithm on images collected for the robust-reading competitions at ICDAR 2003.

Automated image quality assessment for camera-captured OCR

Camera-captured optical character recognition (OCR) is a challenging area because of artifacts introduced during image acquisition with consumer-domain hand-held and Smart phone cameras. Critical information is lost if the user does not get immediate feedback on whether the acquired image meets the quality requirements for OCR. To avoid such information loss, we propose a novel automated image quality assessment method that predicts the degree of degradation on OCR. Unlike other image quality assessment algorithms which only deal with blurring, the proposed method quantifies image quality degradation across several artifacts and accurately predicts the impact on OCR error rate. We present evaluation results on a set of machine-printed document images which have been captured using digital cameras with different degradations.

Stochastic Segment Modeling for Offline Handwriting Recognition

In this paper, we present a novel approach for incorporating structural information into the hidden Markov Modeling (HMM) framework for offline handwriting recognition. Traditionally, structural features have been used in recognition approaches that rely on accurate segmentation of words into smaller units (sub-words or characters). However, such segmentation based approaches do not perform well on real-world handwritten images, because breaks and merges in glyphs typically create new connected components that are not observed in the training data. To mitigate the problem of having to derive accurate segmentation from connected components, we present a novel framework where the HMM based recognition system trained on shorter-span features is used to generate the 2-D character images (the “Stochastic Segments”), and then another classifier that uses structural features extracted from the stochastic character segments generates a new set of scores. Finally, the scores from the HMM system and from structural matching are used in combination to generate a hypothesis that is better than the results from either the HMM or from structural matching alone. We demonstrate the efficacy of our approach by reporting experimental results on a large corpus of handwritten Arabic documents.

Finding structure in noisy text: topic classification and unsupervised clustering

This paper addresses two types of classification of noisy, unstructured text such as newsgroup messages: (1) spotting messages containing topics of interest, and (2) automatic conceptual organization of messages without prior knowledge of topics of interest. In addition to applying our hidden Markov model methodology to spotting topics of interest in newsgroup messages, we present a robust methodology for rejecting messages which are off-topic. We describe a novel approach for automatically organizing a large, unstructured collection of messages. The approach applies an unsupervised topic clustering procedure to generate a hierarchical tree of topics.

Recent improvements and performance analysis of ASR and MT in a speech-to-speech translation system

We report on recent ASR and MT work on our English/Iraqi Arabic speech-to-speech translation system. We present detailed results for both objective and subjective evaluations of translation quality, along with a detailed analysis and categorization of translation errors. We also present novel ideas for quantifying the relative importance of different subjective error categories, and for assigning the blame for an error to a particular phrase pair in the translation model.

Recent improvements in BBN's English/Iraqi speech-to-speech translation system

We report on recent improvements in our English/Iraqi Arabic speech-to-speech translation system. User interface improvements include a novel parallel approach to user confirmation which makes confirmation cost-free in terms of dialog duration. Automatic speech recognition improvements include the incorporation of state-of-the-art techniques in feature transformation and discriminative training. Machine translation improvements include a novel combination of multiple alignments derived from various pre-processing techniques, such as Arabic segmentation and English word compounding, higher order N-grams for target language model, and use of context in form of semantic classes and part-of-speech tags.

Robust named entity detection from optical character recognition output

In this paper, we focus on information extraction from optical character recognition (OCR) output. Since the content from OCR inherently has many errors, we present robust algorithms for information extraction from OCR lattices instead of merely looking them up in the top-choice (1-best) OCR output. Specifically, we address the challenge of named entity detection in noisy OCR output and show that searching for named entities in the recognition lattice significantly improves detection accuracy over 1-best search. While lattice-based named entity (NE) detection improves NE recall from OCR output, there are two problems with this approach: (1) the number of false alarms can be prohibitive for certain applications and (2) lattice-based search is computationally more expensive than 1-best NE lookup. To mitigate the above challenges, we present techniques for reducing false alarms using confidence measures and for reducing the amount of computation involved in performing the NE search. Furthermore, to demonstrate that our techniques are applicable across multiple domains and languages, we experiment with optical character recognition systems for videotext in English and scanned handwritten text in Arabic.

Semantic translation error rate for evaluating translation systems

In this paper, we introduce a new metric which we call the semantic translation error rate, or STER, for evaluating the performance of machine translation systems. STER is based on the previously published translation error rate (TER) (Snover et al., 2006) and METEOR (Banerjee and Lavie, 2005) metrics. Specifically, STER extends TER in two ways: first, by incorporating word equivalence measures (WordNet and Porter stemming) standardly used by METEOR, and second, by disallowing alignments of concept words to non-concept words (aka stop words). We show how these features make STER alignments better suited for human-driven analysis than standard TER. We also present experimental results that show that STER is better correlated to human judgments than TER. Finally, we compare STER to METEOR, and illustrate that METEOR scores computed using the STER alignments have similar statistical properties to METEOR scores computed using METEOR alignments.