Table of Contents
What is LSTM in OCR?
In version 4, Tesseract has implemented a Long Short Term Memory (LSTM) based recognition engine. LSTM is a kind of Recurrent Neural Network (RNN). Tesseract library is shipped with a handy command line tool called tesseract. We can use this tool to perform OCR on images and the output is stored in a text file.
Can we build language independent OCR using LSTM networks?
LSTM models show good promise to be used for language-independent OCR. The recognition errors are very low (around 1\%) without using any language model or dictionary correction.
Why is LSTM used in text classification?
Having a good hold over memorizing certain patterns LSTMs perform fairly better. As with every other NN, LSTM can have multiple hidden layers and as it passes through every layer, the relevant information is kept and all the irrelevant information gets discarded in every single cell.
How does Tesseract LSTM work?
Tesseract is an open source text recognition (OCR) Engine, available under the Apache 2.0 license. It can be used directly or by using an API to extract text from images. Text of arbitrary length is a sequence of characters, and such problems are solved by using RNNs and LSTM is a popular form of RNN.
How do you develop optical character recognition?
Steps involved in Optical Character recognition:-
- Extraction of Character boundaries from Image,
- Building a Convolutional Neural Network(ConvNet) in remembering the Character images,
- Loading trained Convolutional Neural Network(ConvNet) Model,
- Consolidating ConvNet predictions of characters.
What is CTC deep learning?
Connectionist temporal classification (CTC) is a type of neural network output and associated scoring function, for training recurrent neural networks (RNNs) such as LSTM networks to tackle sequence problems where the timing is variable.
How does Tesseract OCR works?
Tesseract tests the text lines to determine whether they are fixed pitch. Where it finds fixed pitch text, Tesseract chops the words into characters using the pitch, and disables the chopper and associator on these words for the word recognition step.
How do you make a Tesseract Traineddata?
Overview of Training Process
- Prepare training text.
- Render text to image + box file.
- Make unicharset file.
- Make a starter traineddata from the unicharset and optional dictionary data.
- Run tesseract to process image + box file to make training data set.
- Run training on training data set.
- Combine data files.
Should we use Lan-guage or LSTM for OCR?
However, using a lan-guage model complicates training of OCR systems, and it also narrows the range of texts that an OCR system can be used with. Recent results have shown that Long Short-Term Memory (LSTM) based OCR yields low error rates even without language modeling.
What is the difference between RNN and LSTM?
Hence, the RNN doesn’t learn the long-range dependencies across time steps. This makes them not much useful. We need some sort of Long term memory, which is just what LSTMs provide. Long-Short Term Memory networks or LSTMs are a variant of RNN that solve the Long term memory problem of the former.
Can long short-term memory models be used for multilingual OCR without language models?
Recent results have shown that Long Short-Term Memory (LSTM) based OCR yields low error rates even without language modeling. In this paper, we explore the question to what extent LSTM models can be used for multilingual OCR without the use of language models.
How do the most popular RNNs work?
Let’s study now how the most popular RNN work. They are the LSTM networks and their structure is as follows: But first: Why are they the most popular ones? It turns out that conventional RNNs have memory problems. Specially designed memory networks are incapable of long-term memory.