1,213 research outputs found
Implicit Language Model in LSTM for OCR
Neural networks have become the technique of choice for OCR, but many aspects
of how and why they deliver superior performance are still unknown. One key
difference between current neural network techniques using LSTMs and the
previous state-of-the-art HMM systems is that HMM systems have a strong
independence assumption. In comparison LSTMs have no explicit constraints on
the amount of context that can be considered during decoding. In this paper we
show that they learn an implicit LM and attempt to characterize the strength of
the LM in terms of equivalent n-gram context. We show that this implicitly
learned language model provides a 2.4\% CER improvement on our synthetic test
set when compared against a test set of random characters (i.e. not naturally
occurring sequences), and that the LSTM learns to use up to 5 characters of
context (which is roughly 88 frames in our configuration). We believe that this
is the first ever attempt at characterizing the strength of the implicit LM in
LSTM based OCR systems
Russian Lexicographic Landscape: a Tale of 12 Dictionaries
The paper reports on quantitative analysis of 12 Russian dictionaries at three levels: 1) headwords: The size and overlap of word lists, coverage of large corpora, and presence of neologisms; 2) synonyms: Overlap of synsets in different dictionaries; 3) definitions: Distribution of definition lengths and numbers of senses, as well as textual similarity of same-headword definitions in different dictionaries. The total amount of data in the study is 805,900 dictionary entries, 892,900 definitions, and 84,500 synsets. The study reveals multiple connections and mutual influences between dictionaries, uncovers differences in modern electronic vs. traditional printed resources, as well as suggests directions for development of new and improvement of existing lexical semantic resources
Machine Reading the Primeros Libros
Early modern printed books pose particular challenges for automatic transcription: uneven inking, irregular orthographies, radically multilingual texts. As a result, modern efforts to transcribe these documents tend to produce the textual gibberish commonly known as "dirty OCR" (Optical Character Recognition). This noisy output is most frequently seen as a barrier to access for scholars interested in the computational analysis or digital display of transcribed documents. This article, however, proposes that a closer analysis of dirty OCR can reveal both historical and cultural factors at play in the practice of automatic transcription. To make this argument, it focuses on tools developed for the automatic transcription of the Primeros Libros collection of sixteenth century Mexican printed books. By bringing together the history of the collection with that of the OCR tool, it illustrates how the colonial history of these documents is embedded in, and transformed by, the statistical models used for automatic transcription. It argues that automatic transcription, itself a mechanical and practical tool, also has an interpretive effect on transcribed texts that can have practical consequences for scholarly work
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