What is the difference between Tesseract 4 and Tesseract 5?
I could only install tesseract 4.2 and I am worry about problems with the performance because this is not the last version.
Tesseract 4 and Tesseract 5 are two versions of the popular open-source optical character recognition (OCR) engine. The main differences between the two versions are:
Improved accuracy: Tesseract 5 has improved accuracy compared to Tesseract 4, particularly for scripts that are not Latin-based.
New language support: Tesseract 5 supports more languages compared to Tesseract 4.
Better layout analysis: Tesseract 5 has improved layout analysis capabilities compared to Tesseract 4, including the ability to recognize table structures and perform text-to-table conversion.
LSTM-based OCR engine: Tesseract 5 uses a Long Short-Term Memory (LSTM) based OCR engine, while Tesseract 4 used a traditional OCR engine.
User-friendly training tools: Tesseract 5 has improved training tools that make it easier for users to train the OCR engine for their specific use case.
Overall, Tesseract 5 is an improvement over Tesseract 4 in terms of accuracy and features.
The speed difference between Tesseract 4 and Tesseract 5 depends on several factors, including the complexity of the input image, the language being recognized, and the performance of the underlying hardware. In general, Tesseract 5 is slower than Tesseract 4 due to the more complex LSTM-based OCR engine it uses. However, the improved accuracy and layout analysis capabilities of Tesseract 5 may offset the reduced speed for some use cases.
It's worth noting that the difference in speed between the two versions may not be significant for small to medium-sized images, but it could become more pronounced for large or complex images. Additionally, Tesseract 5 has a GPU-accelerated version that can speed up processing times compared to the CPU-only version.
With a good usage of all possible tesseract configurations acording to your problem, you should not be worried about performance issues.
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I have a dilemma. If you have only one type of invoice/document, and you have a specific field that you want to process from that invoice and use somewhere else (that filed happens to be a handwritten digit, sometimes written with dashes or slashes), would you use some OCR software or build your own CNN for recognizing the digits? What accuracy would you expect from OCR? Would your CNN be more accurate, as you are just interested in a specific type of digit writing, with specific image dimensions, etc. What would be better in the given situation?
Keep in mind, that you would not use it in any other way, or any other place for handwritten digits recognition, and you already have up to 100k and more documents that are copied to a computer by a human, and you can use it for training and testing.
Thank you.
I would definitely go for a CNN based solution. Since the structure of your document is consistent:
Extract the desired portion of the document with a standard computer vision approach
Train a CNN on an annotated set of a few thousand documents. You should even be able to finetune an existing CNN trained on MNIST and this would require less training images.
This approach should give you >99% accuracy without much effort. The accuracy of the OCR solution really depends on which library you use and the preprocessing you implement.
I am looking for detection/localization CNNs to run within the NVidia DIGITS training platform. So far it seems they only support their homebrew DetectNet for this purpose. Looking around it seems that other SOTA networks such as faster-RCNN, SSD, and YOLO might compete with DetectNet in terms of performance and accuracy, but it does not look like they currently have any support in DIGITS. (Faster-RCNN has a fairly popular implementation, but it is run out of a version of Caffe not supported by DIGITS.)
If anyone has had any success obtaining and using SOTA detection networks with NVidia DIGITS, would you mind supplying links/documentation regarding?
Currently, NVIDIA provides NVIDIA Transfer Learning Toolkit which is useful for training CNNs. NVIDIA provides SSD, Faster RCNN, DetectNet, etc. You can find more here: https://developer.nvidia.com/transfer-learning-toolkit
I'm looking for a way to extract features from audio where I said a digit for speech recognition of the digits 1-10 using backpropagation with neural networks (10 samples for each digit and 5 samples of each digit for testing).
I tried using raw audio data and I also tried feeding the data after fft, and feeding the data with only the ten top frequencies and failed.
Can you suggest a way to extract features of the audio which will help the neural network to gain reasonable results? It's a simple project so I'm not aiming for extremely high performance, but a reasonable performance to demonstrate the ability of such network to learn.
Why don't you try MFCCs ? MFCCs is de facto a standard in ASR.
They weren't design with DNN in mind, but they proved to work with several other ASR implementation (most notably, HMM).
I have a couple slightly modified / non-traditional setups for feedforward neural networks which I'd like to compare for accuracy against the ones used professionally today. Are there specific data sets, or types of data sets, which can be used as a benchmark for this? I.e. "the style of ANN typically used for such-and-such a task is 98% accurate against this data set." It would be great to have a variety of these, a couple for statistical analysis, a couple for image and voice recognition, etc.
Basically, is there a way to compare an ANN I've put together against ANNs used professionally, across a variety of tasks? I could pay for data or software, but would prefer free of course.
CMU has some benchmarks for neural networks: Neural Networks Benchmarks
The Fast Artificial Neural Networks library (FANN) has some benchmarks that are widely used: FANN. Download the source code (version 2.2.0) and look at the directory datasets, the format is very simple. There is always a training set (x.train) and a test set (x.test). At the beginning of the file is the number of instances, the number of inputs and the number of outputs. The next lines are the input of the first instance and the output of the first instance and so on. You can find example programs with FANN in the directory examples. I think they even had detailed comparisons to other libraries in previous versions.
I think most of FANN's benchmarks if not all are from Proben1. Google for it, there is a paper from Lutz Prechelt with detailed descriptions and comparisons.
I work on an audio processing project that needs to do a lot of basic computations (+, -, *) like a FFT (Fast Fourier Transform) calculation.
We're considering using a graphics card to accelerate these computations. But we don't know if this is the best solution. Our desired solution needs to be a good computation system costing less than $500.
We use Matlab programming, and we have a sound card acquisition which have to be plug in the system.
Do you know a solution other than graphics card + motherboard to do lot of calculus?
You can use the free Matlab CUDA library to perform the computations on the GPU. $500 will give you a very decent NVIDIA GPU. Beware that GPU's have limited video memory and will run out of memory with large data volumes even faster than Matlab.
I have benchmarked an 8core intel CPU against an 8800 Nvidia GPU (128streams) with GPUMat , for 512Kb datasets the GPU spun out at the same speed as the 8 core intel at 2Ghz, including transfer times to the GPU memory. For serious GPU work I recommend a dedicated card compared to the one you are using to drive the monitor. Use the motherboard cheapie intel video to drive the monitor and pass the array computes to the Nvidia.
Parallel Computing Toolbox from MathWorks now includes GPU support. In particular, elementwise operations and arithmetic are supported, as well as 1- and 2-dimensional FFTs (along with a whole bunch of other stuff to support hand-written CUDA code if you have that). If you're interested in performing calculations in double-precision, the recent Tesla and Quadro branded cards will give you the best performance.
Here's a trivial example showing how you might use the GPU in MATLAB using Parallel Computing Toolbox:
gA = gpuArray( rand(1000) );
gB = fft( 1 + gA * 3 );