首先将tesseract OCR 4.0安装,配置系统环境变量,python安装pytesseract包,并且在代码里面指定pytesseract.tesseract_cmd的路径。 # 首先部署后,对每个图片进行OCR识别,主要使用命令 tesseract img --psm 6 -l eng 得到OCR识别结果,将识别错误的图片挑出来进行fine-tuning. 我分别围绕
PYTHONIOENCODING=utf-8 python3 $(GENERATE_BOX_SCRIPT) -i "$*.png" -t "$*.gt.txt" > "$@" %.box: %.bin.png %.gt.txt PYTHONIOENCODING=utf-8 python3 $(GENERATE_BOX_SCRIPT) -i "$*.bin.png" -t "$*.gt.txt" > "$@" %.box: %.nrm.png %.gt.txt PYTHONIOENCODING=utf-8 py...
1)模型固定,可以对计算图进行优化 2) 输入输出大小固定,可以做memory优化(注意:有一个概念是fine-tuning,即训练好的模型继续调优,只是在已有的模型做小的改动,本质上仍然是训练(Training)的过程,TensorRT没有fine-tuning 2. 推断(Inference)的batch size要小很多,仍然是latency的问题,因为如果batch size很大,吞吐可...
After adding a new training tool and training the model with a lot of data and fonts, Tesseract achieves better performance. Still, not good enough to work on handwritten text and weird fonts. It is possible to fine-tune or retrain top layers for experimentation. Installing Tesseract Installing...
二值化这个是最有效的，文字分行分块处理， 调用psm=PSM.SINGLE_BLOCK自己训练，4.0可以fine tuning
Fine-tuning: select (and install) aSTART_MODEL From scratch: specify aNET_SPEC(seedocumentation) Change directory assumptions To override the default path name requirements, just set the respective variables in the above list: make training MODEL_NAME=name-of-the-resulting-model DATA_DIR=/data GRO...
一旦你的模型完成初始训练,你可以考虑对其进行微调(fine-tuning)以增强其在特定任务或领域的性能。将此步骤视为使用额外的调味料来完善你的菜肴以适应其风味。 微调涉及在特定于任务的数据集上训练模型,以补充原始训练数据。例如,如果你最初训练了通用语言模型,则可以在与客户支持对话相关的数据集上对其进行微调,以使...
4.1.0 TESSDATA_REPO Tesseract model repo to use. Default: _best TESSDATA Path to the .traineddata directory to start finetuning from. Default: ./usr/share/tessdata GROUND_TRUTH_DIR Ground truth directory. Default: data/MODEL_NAME-ground-truth OUTPUT_DIR Output directory for generated files. ...
* Made some fine tuning to the hOCR output. * Added TSV as another optional output format. * Fixed ABI break introduced in 3.04.00 with the AnalyseLayout() method. * text2image tool - Enable all OpenType ligatures available in a font. This feature requires Pango 1.38 or newer. *...
Cut off the top layer (or some arbitrary number of layers) from the network and retrain a new top layer using the new data. If fine-tuning doesn’t work, this is most likely the next best option. If you start with the most similar-looking script, cutting off the top layer could stil...