| thumbnail: "url to a thumbnail used in social sharing" | |
| tags: | |
| - tag1 | |
| - tag2 | |
| license: apache-2.0 | |
| datasets: | |
| - dataset1 | |
| - dataset2 | |
| metrics: | |
| - metric1 | |
| - metric2 | |
| Model Architecture: | |
| The mychen76/mistral7b_ocr_to_json_v1 (LLM) is a finetuned for convert OCR text to Json object task. this experimental model is based on Mistral-7B-v0.1 which outperforms Llama 2 13B on all benchmarks tested. | |
| Motivation: | |
| Currently, OCR engines are well tested on image detection and text recognition. LLM models are well trained for text processing and generation. Hence, leveraging outputs from OCR engines could save LLM training times for image-to-text use cases such as invoice or receipt image to JSON object conversion tasks. | |
| Model Usage: | |
| Take an invoice or receipt image, perform OCR on the image to get text boxes, and feed the outputs into LLM models to generate a well-formed receipt JSON object. | |
| ``` | |
| ### Instruction: | |
| You are POS receipt data expert, parse, detect, recognize and convert following receipt OCR image result into structure receipt data object. | |
| Don't make up value not in the Input. Output must be a well-formed JSON object.```json | |
| ### Input: | |
| [[[[184.0, 42.0], [278.0, 45.0], [278.0, 62.0], [183.0, 59.0]], ('BAJA FRESH', 0.9551795721054077)], [[[242.0, 113.0], [379.0, 118.0], [378.0, 136.0], [242.0, 131.0]], ('GENERAL MANAGER:', 0.9462024569511414)], [[[240.0, 133.0], [300.0, 135.0], [300.0, 153.0], [240.0, 151.0]], ('NORMAN', 0.9913229942321777)], [[[143.0, 166.0], [234.0, 171.0], [233.0, 192.0], [142.0, 187.0]], ('176 Rosa C', 0.9229503870010376)], [[[130.0, 207.0], [206.0, 210.0], [205.0, 231.0], [129.0, 228.0]], ('Chk 7545', 0.9349349141120911)], [[[283.0, 215.0], [431.0, 221.0], [431.0, 239.0], [282.0, 233.0]], ("Dec26'0707:26PM", 0.9290117025375366)], [[[440.0, 221.0], [489.0, 221.0], [489.0, 239.0], [440.0, 239.0]], ('Gst0', 0.9164432883262634)], [[[164.0, 252.0], [308.0, 256.0], [308.0, 276.0], [164.0, 272.0]], ('TAKE OUT', 0.9367803335189819)], [[[145.0, 274.0], [256.0, 278.0], [255.0, 296.0], [144.0, 292.0]], ('1 BAJA STEAK', 0.9167789816856384)], [[[423.0, 282.0], [465.0, 282.0], [465.0, 304.0], [423.0, 304.0]], ('6.95', 0.9965073466300964)], [[[180.0, 296.0], [292.0, 299.0], [292.0, 319.0], [179.0, 316.0]], ('NO GUACAMOLE', 0.9631438255310059)], [[[179.0, 317.0], [319.0, 322.0], [318.0, 343.0], [178.0, 338.0]], ('ENCHILADO STYLE', 0.9704310894012451)], [[[423.0, 325.0], [467.0, 325.0], [467.0, 347.0], [423.0, 347.0]], ('1.49', 0.988395631313324)], [[[159.0, 339.0], [201.0, 341.0], [200.0, 360.0], [158.0, 358.0]], ('CASH', 0.9982023239135742)], [[[417.0, 348.0], [466.0, 348.0], [466.0, 367.0], [417.0, 367.0]], ('20.00', 0.9921982884407043)], [[[156.0, 380.0], [200.0, 382.0], [198.0, 404.0], [155.0, 402.0]], ('FOOD', 0.9906187057495117)], [[[426.0, 390.0], [468.0, 390.0], [468.0, 409.0], [426.0, 409.0]], ('8.44', 0.9963030219078064)], [[[154.0, 402.0], [190.0, 405.0], [188.0, 427.0], [152.0, 424.0]], ('TAX', 0.9963871836662292)], [[[427.0, 413.0], [468.0, 413.0], [468.0, 432.0], [427.0, 432.0]], ('0.61', 0.9934712648391724)], [[[153.0, 427.0], [224.0, 429.0], [224.0, 450.0], [153.0, 448.0]], ('PAYMENT', 0.9948703646659851)], [[[428.0, 436.0], [470.0, 436.0], [470.0, 455.0], [428.0, 455.0]], ('9.05', 0.9961490631103516)], [[[152.0, 450.0], [251.0, 453.0], [250.0, 475.0], [152.0, 472.0]], ('Change Due', 0.9556287527084351)], [[[420.0, 458.0], [471.0, 458.0], [471.0, 480.0], [420.0, 480.0]], ('10.95', 0.997236430644989)], [[[209.0, 498.0], [382.0, 503.0], [381.0, 524.0], [208.0, 519.0]], ('$2.000FF', 0.9757758378982544)], [[[169.0, 522.0], [422.0, 528.0], [421.0, 548.0], [169.0, 542.0]], ('NEXT PURCHASE', 0.962527871131897)], [[[167.0, 546.0], [365.0, 552.0], [365.0, 570.0], [167.0, 564.0]], ('CALL800 705 5754or', 0.926964521408081)], [[[146.0, 570.0], [416.0, 577.0], [415.0, 597.0], [146.0, 590.0]], ('Go www.mshare.net/bajafresh', 0.9759786128997803)], [[[147.0, 594.0], [356.0, 601.0], [356.0, 621.0], [146.0, 614.0]], ('Take our brief survey', 0.9390400648117065)], [[[143.0, 620.0], [410.0, 626.0], [409.0, 647.0], [143.0, 641.0]], ('When Prompted, Enter Store', 0.9385656118392944)], [[[142.0, 646.0], [408.0, 653.0], [407.0, 673.0], [142.0, 666.0]], ('Write down redemption code', 0.9536812901496887)], [[[141.0, 672.0], [409.0, 679.0], [408.0, 699.0], [141.0, 692.0]], ('Use this receipt as coupon', 0.9658807516098022)], [[[138.0, 697.0], [448.0, 701.0], [448.0, 725.0], [138.0, 721.0]], ('Discount on purchases of $5.00', 0.9624248743057251)], [[[139.0, 726.0], [466.0, 729.0], [466.0, 750.0], [139.0, 747.0]], ('or more,Offer expires in 30 day', 0.9263916611671448)], [[[137.0, 750.0], [459.0, 755.0], [459.0, 778.0], [137.0, 773.0]], ('Good at participating locations', 0.963909924030304)]] | |
| ### Output: | |
| ``` | |
| ```json | |
| { | |
| "receipt": { | |
| "store": "BAJA FRESH", | |
| "manager": "GENERAL MANAGER: NORMAN", | |
| "address": "176 Rosa C", | |
| "check": "Chk 7545", | |
| "date": "Dec26'0707:26PM", | |
| "tax": "Gst0", | |
| "total": "20.00", | |
| "payment": "CASH", | |
| "change": "0.61", | |
| "discount": "Discount on purchases of $5.00 or more,Offer expires in 30 day", | |
| "coupon": "Use this receipt as coupon", | |
| "survey": "Take our brief survey", | |
| "redemption": "Write down redemption code", | |
| "prompt": "When Prompted, Enter Store Write down redemption code Use this receipt as coupon", | |
| "items": [ | |
| { | |
| "name": "1 BAJA STEAK", | |
| "price": "6.95", | |
| "modifiers": [ | |
| "NO GUACAMOLE", | |
| "ENCHILADO STYLE" | |
| ] | |
| }, | |
| { | |
| "name": "TAKE OUT", | |
| "price": "1.49" | |
| } | |
| ] | |
| } | |
| } | |
| ``` | |
| # Load model directly | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| tokenizer = AutoTokenizer.from_pretrained("mychen76/mistral7b_ocr_to_json_v1") | |
| model = AutoModelForCausalLM.from_pretrained("mychen76/mistral7b_ocr_to_json_v1") | |
| prompt=f"""### Instruction: | |
| You are POS receipt data expert, parse, detect, recognize and convert following receipt OCR image result into structure receipt data object. | |
| Don't make up value not in the Input. Output must be a well-formed JSON object.```json | |
| ### Input: | |
| {receipt_boxes} | |
| ### Output: | |
| """ | |
| with torch.inference_mode(): | |
| inputs = tokenizer(prompt,return_tensors="pt",truncation=True).to(device) | |
| outputs = model.generate(**inputs, max_new_tokens=512) | |
| result_text = tokenizer.batch_decode(outputs)[0] | |
| print(result_text) | |
| ``` | |
| ## Get OCR Image boxes | |
| ```python | |
| from paddleocr import PaddleOCR, draw_ocr | |
| from ast import literal_eval | |
| import json | |
| paddleocr = PaddleOCR(lang="en",ocr_version="PP-OCRv4",show_log = False,use_gpu=True) | |
| def paddle_scan(paddleocr,img_path_or_nparray): | |
| result = paddleocr.ocr(img_path_or_nparray,cls=True) | |
| result = result[0] | |
| boxes = [line[0] for line in result] #boundign box | |
| txts = [line[1][0] for line in result] #raw text | |
| scores = [line[1][1] for line in result] # scores | |
| return txts, result | |
| # perform ocr scan | |
| receipt_texts, receipt_boxes = paddle_scan(paddleocr,receipt_image_array) | |
| print(50*"--","\ntext only:\n",receipt_texts) | |
| print(50*"--","\nocr boxes:\n",receipt_boxes) | |
| ``` | |
| # Load model in 4bits | |
| ```python | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, BitsAndBytesConfig | |
| # quantization_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True) | |
| bnb_config = BitsAndBytesConfig( | |
| llm_int8_enable_fp32_cpu_offload=True, | |
| load_in_4bit=True, | |
| bnb_4bit_use_double_quant=True, | |
| bnb_4bit_quant_type="nf4", | |
| bnb_4bit_compute_dtype=torch.bfloat16, | |
| ) | |
| # control model memory allocation between devices for low GPU resource (0,cpu) | |
| device_map = { | |
| "transformer.word_embeddings": 0, | |
| "transformer.word_embeddings_layernorm": 0, | |
| "lm_head": 0, | |
| "transformer.h": 0, | |
| "transformer.ln_f": 0, | |
| "model.embed_tokens": 0, | |
| "model.layers":0, | |
| "model.norm":0 | |
| } | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # model use for inference | |
| model_id="mychen76/mistral7b_ocr_to_json_v1" | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_id, | |
| trust_remote_code=True, | |
| torch_dtype=torch.float16, | |
| quantization_config=bnb_config, | |
| device_map=device_map) | |
| # tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) | |
| ``` | |
| Dataset use for finetuning: | |
| mychen76/invoices-and-receipts_ocr_v1 | |